Salivary mRNA Profiling, Biomarkers and Related Methods and Kits of Parts

ABSTRACT

A method to detect a biomarker in saliva wherein the biomarker is an extracellular mRNA, comprises detecting the extracellular mRNA in the cell-free saliva; transcriptome analysis of saliva comprises detecting a transcriptome pattern in the cell-free saliva; a method to detect genetic alterations in an organ or in a gene in the organ by analyzing saliva, comprises detecting a transcriptome pattern and/or the mRNA profiling of the gene in cell-free saliva; a method to diagnose an oral or systemic pathology disease or disorder in a subject, comprises: detecting profile of a biomarker associated with the pathology disease or disorder, in particular mRNA and/or protein, in cell-free saliva and/or serum; kits comprising identifier for at least one biomarker for performing at least one of the methods; and use of salivary biomarker salivary and/or serum mRNAs as biomarkers for oral and/or systemic pathology, disease or disorder.

This invention was made with Government support of grant U01-DE15018awarded by the NIH. The Government has certain rights on this invention

FIELD OF THE DISCLOSURE

The present disclosure relates to profiling of biomarkers and to methodand kits using said biomarkers. In particular, the present disclosurerelated to biomarkers for detection of cancer and in particular of OralCavity and Oropharyngeal squamous Cell Carcinoma (OSCC).

BACKGROUND OF THE DISCLOSURE

Biomarkers are molecular indicators of a specific biological property, abiochemical feature or facet that can be used to measure the progress ofdisease or the effects of treatment.

Proteins and nucleic acids are exemplary biomarkers. In particular, ithas been widely accepted that genomic messengers detectedextracellularly can serve as biomarkers for diseases [6]. In particular,nucleic acids have been identified in most bodily fluids includingblood, urine and cerebrospinal fluid, and have been successfully adoptedfor using as diagnostic biomarkers for diseases [28, 42, 49].

Saliva is not a passive “ultrafiltrate” of serum [41], but contains adistinctive composition of enzymes, hormones, antibodies, and othermolecules. In the past 10 years, the use of saliva as a diagnostic fluidhas been successfully applied in diagnostics and predicting populationsat risk for a variety of conditions [47].

Specific and informative biomarkers in saliva are desirable to serve fordiagnosing disease and monitoring human health [30, 47, 6]. For examplebiomarkers have been identified in saliva for monitoring caries,periodontitis, oral cancer, salivary gland diseases, and systemicdisorders, e.g., hepatitis and HIV [35]. Also previous studies show thathuman DNA biomarkers can be identified in saliva and used for oralcancer detection [30, 36]. RNA is more labile than DNA and is presumedto be highly susceptible to degradation by RNases. Furthermore, RNaseactivity, is reported to be elevated in saliva, which constitutes aninexpensive, non-invasive and accessible bodily fluid suitable to act asan ideal diagnostic medium. In particular, RNAase activity is reportedto be elevated in saliva of cancer patients [83]. It has, thus, beencommonly presumed that human mRNA could not survive extracellularly insaliva. OSCC is the sixth most common cancer in the world, and affects50,000 Americans annually. Worldwide, cancers of the oral cavity andoropharynx represent a great public health problem. OSCC accounts fornearly 50% of all newly diagnosed cancers in India and is a leadingcause of death in France [1].

Despite improvements in locoregional control, morbidity and mortalityrates have improved little in the past 30 years [2]. Therefore, earlydetection or prevention of this disease is likely to be most effective.Detecting OSCC at an early stage is believed to be the most effectivemeans to reduce death and disfigurement from this disease. The absenceof definite early warning signs for most head and neck cancers suggeststhat sensitive and specific biomarkers are likely to be important inscreening high risk patients.

SUMMARY OF THE DISCLOSURE

According to a first aspect, a method to detect a biomarker in a bodilyfluid including a cell phase and a fluid phase, wherein the biomarker isan extracellular mRNA and bodily fluid is saliva, preferablyunstimulated saliva, is disclosed. The method comprises: providing acell-free fluid phase portion of the bodily fluid; and detecting theextracellular mRNA in the cell-free fluid phase portion of the bodilyfluid.

In particular, detecting the extracellular mRNA can comprise: isolatingthe extracellular mRNA from the cell-free fluid phase portion of thebodily fluid, and amplifying the extracellular mRNA.

According to a second aspect, transcriptome analysis of a bodily fluid,including a cell phase and a fluid phase, wherein the bodily fluid issaliva, is disclosed. The method comprises: providing a cell-free fluidphase portion of the bodily fluid; and detecting a transcriptome patternin the cell-free fluid phase portion of the bodily fluid. The bodilyfluid is preferably unstimulated saliva.

In particular, detecting transcriptome pattern in the saliva supernatantis preferably performed by microarray assay, most preferably byhigh-density oligonucleotide microarray assay. Detecting transcriptomepattern in the saliva supernatant can also performed by quantitative PCRanalysis or RT-PCR analysis.

According to a third aspect, a method to detect genetic alterations inan organ by analyzing a bodily fluid draining from the organ andincluding a cell phase and a fluid phase, is disclosed. The bodily fluidis in particular saliva, preferably unstimulated saliva and methodcomprises: providing cell-free fluid phase portion of the bodily fluid;detecting a transcriptome pattern in the cell-free fluid phase portionof the bodily fluid; and comparing the transcriptome pattern with apredetermined pattern, the predetermined pattern being indicative of acommon transcriptome pattern of normal cell-free fluid phase portion ofthe bodily fluid.

According to a fourth aspect, a method to detect genetic alteration of agene in an organ by analyzing a bodily fluid draining from the organ andincluding a cell phase and a fluid phase, is disclosed. The bodily fluidis in particular saliva and the method comprises: providing a cell-freefluid phase portion of the bodily fluid; detecting an mRNA profile ofthe gene in the cell-free fluid phase portion of the bodily fluid; andcomparing the mRNA profile of the gene with a predetermined mRNA profileof the gene, the predetermined mRNA profile of the gene being indicativeof the mRNA profile of the gene in normal cell-free fluid phase portionof the bodily fluid.

According to a fifth aspect, a method to diagnose an oral or systemicpathology disease or disorder in a subject, is disclosed. The methodcomprises: providing a cell-free fluid phase portion of the saliva orthe subject, detecting in the provided cell-free saliva fluid phaseportion an mRNA profile of a gene associated with the pathology, diseaseor disorder; and comparing the RNA profile of the gene with apredetermined mRNA profile of the gene, the predetermined mRNA profileof the gene being indicative of the presence of the pathology, disease,or disorder in the subject.

In a first embodiment the pathology, disease or disorder is a cancer ofthe oral cavity and/or of oropharynx, the bodily fluid is saliva and thegene is selected from the group consisting of the gene coding for IL8(Interleukin 8), IL1B (Interleukin 1, beta), DUSP1 (Dual specificityphosphatase 1), H3F3A (H3 histone, family 3A), OAZ1 (Ornithinedecarboxylase antizyme 1), S100P (S100 calcium binding protein P) andSAT (Spermidine/spermine N1-acetyltransferase).

In a second embodiment the pathology, disease or disorder is a cancer ofthe oral cavity and/or of oropharynx, the bodily fluid is blood serumand the gene is selected IL6 (interleukin 6), H3F3A, TPT1 (Tumor proteintranslationally controlled 1), FTH1 (Ferritin heavy polypeptide 1),NCOA4 (Nuclear receptor coactivator 4) and ARCR (Ras homolog genefamily, member A).

Diseases that can be diagnosed include oropharyngeal squamous cellcarcinoma and possibly other systemic diseases.

According to a sixth aspect, a method to diagnose an oral or systemicpathology, disease or disorder in a subject is disclosed. The methodcomprises: providing a cell-free fluid phase portion of the saliva ofthe subject; detecting in the provided cell-free fluid phase portion atranscriptome pattern associated with the pathology, disease ordisorder; and comparing the transcriptome pattern with a predeterminedpattern, recognition in the transcriptome pattern of characteristics ofthe predetermined pattern being diagnostic for the pathology, disease ordisorder in the subject.

In an embodiment, the pathology, disease or disorder is a cancer of theoral cavity and/or of oropharynx, and transcriptome include transcriptis selected from the group consisting of transcripts for IL8, IL1B,DUSP1, H3F3A, OAZ1, S1 OOP, SAT from saliva.

According to a seventh aspect, a method to diagnose an oral or systemicpathology, disease or disorder in a subject is disclosed, the methodcomprising: providing serum of the subject; detecting in the providedserum a transcriptome pattern associated with the pathology, disease ordisorder, and comparing the transcriptome pattern with a predeterminedpattern, recognition in the transcriptome pattern of characteristics ofthe predetermined pattern being diagnostic for the pathology, disease ordisorder in the subject.

In an embodiment, the pathology, disease or disorder is a cancer of theoral cavity and/or of oropharynx, and transcriptome include transcriptis selected from the group consisting of transcripts for IL6, H3F3A,TPT1, FTH1, NCOA4 and ARCR from serum.

Diseases that can be diagnosed include oropharyngeal squamous cellcarcinoma possibly other systemic diseases.

According to a eight aspect, a method for diagnosing a cancer, in asubject is disclosed. The method comprises: providing a bodily fluid ofthe subject; detecting in the bodily fluid a profile of a biomarker,comparing the profile of the biomarker with a predetermined profile ofthe biomarker, recognition in the profile of the biomarker ofcharacteristics of the predetermined profile of the biomarker beingdiagnostic for the cancer.

Pathologies, diseases or disorders that can be diagnosed includeoropharyngeal squamous cell carcinoma and possibly other systemicdiseases. Biomarkers include IL8, IL1B, DUSP1, H3F3A, OAZ1, S100P, SAT,IL6, H3F3A, TPT1, FTH1, NCOA4 and ARCR.

In a first embodiment, the pathology, disease or disorder isoropharyngeal squamous cell carcinoma, the biomarker is selected fromthe group consisting of IL8 IL1B, DUSP1, H3F3A, OAZ1, S100P, SAT, thebodily fluid is saliva and detecting a profile or a biomarker isperformed by detecting the mRNA profile of the biomarker.

In a second embodiment, the pathology, disease or disorder isoropharyngeal squamous cell carcinoma, the biomarker is selected fromthe group consisting of IL6, H3F3A, TPT1, FTH1, NCOA4 and ARCR thebodily fluid is serum and detecting a profile of a biomarker isperformed by detecting the mRNA profile of the biomarker.

In a third embodiment, the pathology, disease or disorder isoropharyngeal squamous cell carcinoma, the biomarker is IL6, the bodilyfluid is blood serum and detecting a profile of a biomarker is performedby detecting the protein profile of the biomarker

According to an eighth aspect, a kit for the diagnosis of an oral and/orsystemic pathology, disease or disorder is disclosed, the kitcomprising: an identifier of at least one biomarker in a bodily fluid,the biomarker selected from the group consisting of IL8, IL1B, DUSP1,H3F3A, OAZ1, S100P, SAT, IL6, H3F3A, TPT1, FTH1, NCOA4 and ARCR; and adetector for the identifier.

Pathologies, diseases or disorders that can be diagnosed includeoropharyngeal squamous cell carcinoma, and possibly the other systemicdiseases.

The identifier and the detector are to be used in detecting the bodilyfluid profile of the biomarker according to the methods hereindisclosed. In particular, the identifier is associated to the biomarkerin the bodily fluid, and the detector is used to detect the identifier,the identifier and the detector thereby enables the detection of thebodily fluid profile of the biomarker.

According to a ninth aspect, a method to diagnose an oral and/orsystemic pathology disease or disorder, is disclosed. The methodcomprising: using salivary and/or serum mRNAs as biomarkers for oraland/or systemic pathology, disease or disorder.

In a preferred embodiment the MRNA codifies for at least one of thebiomarker selected from the group consisting of IL8, IL1B, DUSP1, H3F3A,OAZ1, S100P, SAT, IL6, H3F3A, TPT1, FTH1, NCOA4 and ARCR.

Diseases that can be diagnosed include oropharyngeal squamous cellcarcinoma, and possibly other systemic diseases.

According to a tenth aspect, a method to diagnose an oral and/or systempathology, is disclosed. The method comprising: using salivary or serumproteins as biomarkers for oral and/or systemic pathology, disease ordisorder, in particular IL6 protein in serum and IL8 protein in saliva.

The methods and kits of the disclosure will be exemplified with the aidof the enclosed figures.

DESCRIPTION OF THE FIGURES

FIG. 1A shows results of a RT-PCR typing for ACTB performed on RNAisolated from cell-free saliva supernatant from human beings afterstorage for 1 month (lane 2), 3 months (lane 3) and 6 months (lane 4),with a 100 bp ladder molecular weight marker (lane 1) and a negativecontrol (omitting templates) (lane 5). A molecular size marker isindicated on the left side of the Figure by arrows.

FIG. 1B shows results of a RT-PCR performed on RNA isolated fromcell-free saliva supernatant from human beings (lane 1) and typing GAPDH(B1), RPS9 (B2) and ACTB (B3), with positive control (human total RNA,BD Biosciences Clontech, Palo Alto, Calif., USA) (lane 2) and negativecontrols (omitting templates) (lane 3). A molecular size marker isindicated on the left side of the Figure by arrows.

FIG. 2A shows results of a capillary electrophoresis performed tomonitor RNA amplification from RNA isolated from cell-free salivasupernatant from human beings. Lanes 1 to 5 show 1 kb DNA ladder (lane1), 5 μl saliva after RNA isolation (undetectable) (lane 2), 1 μl tworound amplified cRNA (range from 200 bp to ˜4 kb) (lane 3), 1 μl cRNAafter fragmentation (around 100 bp) (lane 4) and Ambion RNA CenturyMarker (lane 5). A molecular size marker is indicated on the left sideand right side of the Figure by arrows.

FIG. 2B shows results of a PCR performed on RNA isolated from cell-freesaliva supernatant from human beings at various stage of amplificationand typing for ACTB. Lane 1 to 8 shows 100 bp DNA ladder (lane 1), totalRNA isolated from cell-free saliva (lane 2), 1st round cDNA (lane 3),1st round cRNA after RT (lane 4), 2nd round cDNA (lane 5), 2nd roundcRNA after RT (lane 6), positive control (human total RNA, BDBiosciences Clontech, Palo Alto, Calif., USA) (lane 7) and negativecontrol (omitting templates) (lane 8). A molecular size marker isindicated on the left side of the Figure by arrows.

FIG. 2C shows a diagram reporting results of the analysis of target cRNAperformed by Agilent 2100 bioanalyzer before hybridization onmicroarray. On x axis, the molecular weight (bp) of the fragmented cRNAwith reference to the marker RNA, is indicated. On y axis, the quantityof the fragmented cRNA (ug/ml) measurable by a Bioanalyzer, isindicated.

FIG. 3 shows results of a RT-PCR performed on RNA isolated fromcell-free saliva supernatant from human beings (saliva) together with aladder (Mrkr) positive controls (Ctrl(+)) and negative controls(Ctrl(−)) and typing for IL6 (IL6), IL8 (IL8) and β-Actin (β-Actin).

FIG. 4 shows results of a PCR performed for the housekeeping β-actin onwhole saliva, serum samples, and samples that had been centrifuged at0×g (0×g), 1,000×g (1,000×g), 2,600×g (2,600×g), 5,000×g (5,000×g) and10,000×g (10,000×g) using genomic DNA as marker (Mrkr) for cell lysisand spillage of intracellular compounds.

FIG. 5A shows a diagram reporting the mean concentrations of mRNA forIL8 detected in replicate samples by qRT-PCR in saliva from patientswith OSCC (Cancer) and normal subjects (Control). On x axis the samplegroups are reported. On y axis the number of copies detected isreported.

FIG. 5B shows a diagram reporting the mean concentrations of IL8detected in replicate samples by ELISA in saliva from patients with OSCC(Cancer) and normal subjects (Control). On x axis the sample groups arereported. On y axis the concentration expressed in pg/ml, is reported.

FIG. 6A shows a diagram reporting the mean concentrations of mRNA forIL6 detected in replicate samples by qRT-PCR in serum from patients withOSCC (Cancer) and normal subjects (Control). On x axis the sample groupsare reported. On y axis the number of copies detected is reported.

FIG. 6B shows a diagram reporting the mean concentrations of IL6detected in replicate samples by ELISA in serum from patients with OSCC(Cancer) and normal subjects (Control). On x axis, the sample groups arereported. On y axis the concentration expressed in pg/ml, is reported

FIG. 7A shows a diagram reporting the Receiver Operating Characteristic(ROC) curve calculated for IL8 in Saliva. On the x axis 1-specificity isreported. On y axis the sensitivity is reported.

FIG. 7B shows a diagram reporting the ROC curve calculated for IL6 inserum. On the x axis 1-specificity is reported. On y axis thesensitivity is reported.

FIG. 7C shows a diagram reporting the ROC curve calculated for acombination of IL8 in saliva and IL6 in serum. On the x axis1-specificity is reported. On y axis the sensitivity is reported.

FIG. 8 shows results of a PCR reaction performed on serum human mRNAphenotyping of salivary mRNAs for RPS9 (Lane 2, 3 and 4); GAPDH (Lane 5,6 and 7); B2M (Lane 8, 9 and 10) and ACTB (Lane 11, 12 and 13), togetherwith DNA ladder, as a control (Lane 1).

FIG. 9 shows a diagram reporting a ROC curve of the logistic regressionmodel for the circulating mRNA in serum. On the x axis 1-specificity isreported. On y axis the sensitivity is reported.

FIG. 10 shows a diagram reporting the classification and regressiontrees (CART) model assessing the serum mRNA predictors for OSCC.

FIG. 11 shows a diagram reporting a ROC curve of the logistic regressionmodel for the predictive power of combined salivary mRNA biomarkers. Onthe x axis 1-specificity is reported. On y axis the sensitivity isreported.

FIG. 12 shows a diagram reporting the classification and regressiontrees (CART) model assessing the salivary mRNA predictors for OSCC.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

A method to detect an extracellular mRNA in a bodily fluid, is disclosedwherein the bodily fluid is saliva and the extracellular mRNA isdetected in a cell-free fluid phase portion of saliva. Presence of RNAsin the cell-free fluid phase portion of saliva was confirmed by theprocedures extensively described in the Examples, the quality of thedetected mRNA meeting the demand for techniques such as PCR, qPCR, andmicroarray assays.

In the method, detecting extracellular mRNAs herein also informativemRNAs, is performed in a bodily fluid, saliva, that meets the demands ofan inexpensive, non-invasive and accessible bodily fluid to act as anideal medium for investigative analysis.

Detecting informative mRNAs is in particular performed in a portion ofsaliva (cell-free fluid phase) wherein presence of microorganisms andthe extraneous substances such as food debris is minimized, which allowsanalyzing the molecules in simple and accurate fashion. Preferably, thecell-free fluid phase portion of derived from unstimulated saliva.

In the method, the saliva can be collected according to procedures knownin the art and then processed to derive the cell-free fluid phasethereof, for example by centrifugation of the collected saliva, whichresults in a pelleted saliva cell phase and a cell-free saliva fluidphase supernatant. (see procedures extensively described in Examples 1,5 and 13)

According to the present disclosure, the conditions for separating thecell-phase and the fluid phase of saliva are optimized to avoidmechanical rupture of cellular elements which would contribute to theRNA detected in the fluid cell-free phase.

In embodiments wherein the separation is performed by centrifugation,optimization can be performed by testing housekeeping genes on samplescentrifuged at various speed and on whole saliva samples, using DNA as amarker of cell lysis and spillage, to derive the optimizedcentrifugation speed. (See procedure described in Example 5).

Detection of the extracellular mRNA in the cell-free saliva fluid phaseportion (salivary mRNA) can then be performed by techniques known in theart allowing mRNA qualitative and/or a quantitative analysis, such asRT-PCR, Q-PCR and Microarray. The detection can in particular beperformed according to procedures that can include isolation and anamplification of the salivary mRNA and that are exemplified in theExamples.

Detection of the salivary mRNA in the method can be performed for thepurpose of profiling the salivary mRNA.

In a first series of embodiments, the expression of predetermined genes,can be profiled in a cell-free fluid phase portion of saliva. In thoseembodiments, detection of the mRNA profile can be performed by RT-PCR orany techniques allowing identification of a predetermined target mRNA.Quantitative analysis can then be performed with techniques such asQuantitative PCR (Q-PCR) to confirm the presence of mRNA identified bythe RT-PCR. A reference database can then be generated based on the mRNAprofiles so obtained. Exemplary procedures to perform such qualitativeand quantitative analyses of salivary mRNA are described in details inExamples 1, 4 and 9.

In a second series of embodiments, a transcriptome analysis of salivacan be performed by detecting a transcriptome pattern in the cell-freefluid phase portion of saliva. Detection of the transcriptome patterncan be performed by isolating and linearly amplifying salivary mRNA,which can then be profiled with techniques such as high-densityoligonucleotide microarrays. Quantitative analysis can then be performedwith techniques such as Q-PCR to confirm the presence of mRNA in thepattern identified by the microarray. A reference database can then begenerated based on the mRNA profiles so obtained. Exemplary proceduresto perform such qualitative and quantitative analyses of salivary mRNAare described in details in Examples 2-3, 9-10 and 14-15.

Profiling salivary RNA can be performed to detect and/or monitor humanhealth and disease or to investigate biological questions, such as forexample, the origin, release and clearance of mRNA in saliva. Thesalivary mRNA provides actual or potential biomarkers to identifypopulations and patients at high risk for oral and systemic pathologies,diseases or disorders.

Alterations of the salivary mRNA profiles and transcriptome patternscharacterizing the cell-free fluid phase portion of saliva or normalsubjects can be indicative of pathologies, diseases or disorders ofvarious origin. Examples of those pathologies, diseases or disorders areprovided by the inflammatory conditions of the oral cavity, OSCC orother conditions such as diabetes, breast cancer and HIV.

Also comparison between the mRNA profiles and transcriptome patterns ofsubject affected with a determined pathology, disease or disorder, canresult in the identification of informative biomarkers for thedetermined pathology disease or disorder. In particular, salivary mRNAcan be used as diagnostic biomarkers for oral and systemic pathologies,diseases or disorders that may be manifested in the oral cavity.

In particular, salivary mRNA can be used as diagnostic biomarkers forcancer that may be manifested and/or affect the oral cavity.Saliva-based mRNA assays have the needed specificity and sensitivity forreliable diagnostics.

In case of various forms of cancer, alterations of the normal salivarymRNA and transcriptome patterns can also reflect the genetic alterationsin one or more portions of the oral cavity which are associated withpresence of the tumor. For oral cancer patients, the detectedcancer-associated RNA signature is likely to originate from the matchedtumor and/or a systemic response (local or distal) that further reflectsitself in the whole saliva coming from each of the three major sources(salivary glands, gingival crevicular fluid, and oral mucosal cells). Itis conceivable that disease-associated RNA can find its way into theoral cavity via the salivary gland or circulation through the gingivalcrevicular fluid. A good example is the elevated presence of HER-2proteins in saliva of breast cancer patients [87].

A common transcriptome of normal cell-free saliva, includingapproximately 185 different human mRNAs, also defined as Normal SalivaryCore Transcriptome (NSCT) was identified in outcome of a transcriptomeanalysis performed on cell-free fluid phase of saliva from normalsubject (see Example 2, Table 2).

Since the NSCT was identified using the probe sets on HG U133Amicroarray representing only ˜19,000 human genes, and the human genomecomposed of more than 30,000 genes [48], it is expected that more humanmRNAs will be identified in saliva by other methodologies and additionalsalivary patterns are identifiable by the method herein disclosed.

The NSCT and/or other salivary transcriptome patterns in cell-freesaliva from normal populations can serve in a Salivary TranscriptomeDiagnostics (SlvTD), for potential applications in disease diagnosticsas well as normal health surveillance.

Accordingly, in a first embodiment of the SlvTD, a method to diagnose anoral or systemic pathology disease or disorder in a subject, isdisclosed. The method comprises: providing a cell-free fluid phaseportion of the saliva of the subject; detecting in the providedcell-free saliva fluid phase portion an mRNA profile of a geneassociated with the disease; and comparing the RNA profile of the genewith a predetermined mRNA profile of the gene, the predetermined mRNAprofile of the gene being indicative of the presence of the disease inthe subject.

In a second embodiment of the SlvTD, a method to diagnose an oral orsystemic pathology disease or disorder in a subject, is disclosed. Themethod comprises: providing cell-free saliva supernatant of the subject;detecting in the cell-free saliva supernatant a transcriptome patternassociated with the pathology disease or disorder; and comparing thetranscriptome pattern with a predetermined pattern, recognition in thetranscriptome pattern of characteristics of the predetermined patternbeing diagnostic for the pathology disease or disorder in the subject.

In a third embodiment of the SlvTD, a method to identify a biomarkerassociated with a predetermined pathology disease or disorder isdisclosed. The method comprises: detecting a first mRNA profiling of apredetermined gene in cell-free fluid phase portion of saliva of asubject affected by the pathology disease or disorder, detecting asecond mRNA profiling of the predetermined gene in cell-free fluid phaseportion of saliva of a normal subject; comparing the first mRNAprofiling with the second mRNA profiling, recognition of differencesbetween the first mRNA profiling and the second mRNA profiling, thedifferences validated by statistical analysis, being indicative of theidentification of the predetermined gene as a biomarker for thepredetermined pathology disease or disorder.

In particular the difference between the RNA profiling from one diseasecategory to one healthy category is analyzed by microarray statisticalmethodologies. The algorithms used include MAS 5.0, DNA-Chip analyzer1.3 and RMA 3.0. Preferably, the analysis is performed by a combinationof these methods to provide more powerful and accurate markers to test.The markers identified by microarray will then be tested by conventionaltechniques such as Q-PCR.

In a fourth embodiment of the SlvTD a diagnostic method can beperformed, wherein the cell-free saliva is contacted with an identifierfor the presence or expression of the biomarker, and the presence of theidentifier associated to presence or expression of the biomarker isdetected, preferably by means of a detector.

The SlvTD allow detection of diseases such as tumors at a stage earlyenough that treatment is likely to be successful, with screening toolsexhibiting the combined features of high sensitivity and highspecificity. Moreover, the screening tool are sufficiently noninvasiveand inexpensive to allow widespread applicability.

The results of the above methods of the SlvTD can be integrated with acorresponding analysis performed at an mRNA and/or protein level and/orin other bodily fluid, such as blood serum.

Biomarkers, such as protein or transcriptome patterns detected in serumcan also serve in a Serum Transcriptome Diagnostics (SrmTD), forpotential applications in disease diagnostics as well as normal healthsurveillance. Embodiments of the SrmTD include methods corresponding tothe ones reported above for the SlvTD, wherein the bodily fluid analyzedis serum instead of cell-free saliva.

In particular, the results obtained following the SlvTD can be combinedwith results obtained with the SrmTD, in a combined Salivary and SerumTranscriptome approach (SSTD).

According to the SSTD a diagnostic method can be performed, wherein thebodily fluid, serum and/or saliva is contacted with an identifier forthe presence or expression of the biomarker, wherein the biomarker canbe a protein or an mRNA and the presence of the identifier associated topresence or expression of the biomarker is detected, preferably by meansof a detector.

Examples of the SlvTD, SrmTD and SSTD are herein provided with referenceto the OSCC. The person skilled in the art can derive the appropriatemodifications of the STD herein exemplified for diseases different thanOSCC upon reading of the present disclosure.

Profiling of two specific cytokines, IL6 and IL8, was measured in thecell-free fluid phase portion of saliva and serum of patients with OSCCaccording to procedures extensively disclosed in Examples 4-8. IL8 wasdetected at higher concentrations in the saliva of patients with OSCC(P<0.01) and IL6 was detected at higher concentrations in the serum ofpatients with OSCC (P<0.01). These results were confirmed at both themRNA and the protein levels, and the results were concordant. Theconcentration of IL8 in saliva and IL6 in serum did not appear to beassociated with gender, age, or alcohol or tobacco use (P>0.75). Thedata were subjected to statistical analysis, in particular to ROCanalysis, and were able to determine the threshold value, sensitivity,and specificity of each biomarker for detecting OSCC (see Example 8,Table 3). Furthermore, the inventors were able to measure mRNA insalivary specimens.

A transcriptome analysis of unstimulated saliva collected from patientswith OSCC and normal subjects was performed as disclosed in Examples9-12 and in Examples 13-16.

RNA isolation was performed from the saliva supernatant, followed bytwo-round linear amplification with T7 RNA polymerase. Human GenomeU133A microarrays were applied for profiling human salivarytranscriptome. The different gene expression patterns were analyzed bycombining a t test comparison and a fold-change analysis on 10 matchedcancer patients and controls. Quantitative polymerase chain reaction(qPCR) was used to validate the selected genes that showed significantdifference (P<0.01) by microarray. The predictive power of thesesalivary mRNA biomarkers was analyzed by receiver operatingcharacteristic curve and classification models.

The results of a first set of microarray analysis showed that there are1,679 genes exhibited significantly different expression level in salivabetween cancer patients and controls (P<0.05). Seven cancer-related mRNAbiomarkers that exhibited at least a 3.5-fold elevation in OSCC saliva(P<0.01) were consistently validated by qPCR on saliva samples from OSCCpatients (n=32) and controls (n=32). These salivary RNA biomarkers aretranscripts of IL8, IL1B, DUSP1, H3F3A, OAZ1, S 100P, and SAT. Thecombinations of these biomarkers yielded sensitivity (91%) andspecificity (91%) in distinguishing OSCC from the controls. (seeExamples 13-16)

The results of a second set of microarray analysis showed five of tenup-regulated genes selected based on their reported cancer-association,showed significantly elevated transcripts in serum of OSCC patient.These RNA biomarkers are transcripts of H3F3A, TPT1, FTH1, NCOA4 andARCR. The results validated by qPCR confirmed that transcripts of thesefive genes were significantly elevated in the serum of OSCC patient(Wilcoxon Signed Rank test, P<0.05). (See Examples 9 to 12)

Using the described collection and processing protocols, the presence ofACTB, B2W, GAPDH and RPS9 mRNAs (controls mRNA) were confirmed in allserum (patients and controls) by RT-PCR.

Accordingly, a method for diagnosing a cancer, in particular OSCC in asubject, is disclosed. The method comprises: providing a bodily fluidsof the subject; detecting in the bodily fluid a profile of a bit)marker, the biomarker selected from the group consisting of IL8 IL1B,DUSP1, H3F3A, OAZ1, S100P, SAT, IL6, H3F3A, TPT1, FTH1, NCOA4 and ARCR,comparing the profile of the biomarker with a predetermined profile ofthe biomarker, recognition in the profile of the biomarker ofcharacteristics of the predetermined profile of the biomarker beingdiagnostic for the cancer.

Also method to diagnose oral and/or systemic pathology, disease ordisorder, in particular OSCC, is disclosed. The method comprises usingsalivary mRNAs as biomarkers for oral and/or systemic diseases, inparticular salivary mRNAs of selected from the group consisting of IL8IL1B, DUSP1, H3F3A, OAZ1, S100P and SAT.

Additionally a method to diagnose oral and/or systemic pathology,disease or disorder, in particular OSCC, is disclosed. The methodcomprises: using serum MRNAs and/or protein as biomarkers for oraland/or systemic diseases, in particular serum mRNAs of selected from thegroup consisting of IL6, H3F3A, TPT1, FTH1, NCOA4 and ARCR, and serumIL6 protein.

Given the multifactorial nature of oncogenesis and the heterogeneity inoncogenic pathways use of combinations of salivary and/or serumbiomarkers, ensuring higher specificity and sensitivity, to detect thedisease, is preferred. Multiple statistical strategies reported and riskmodels described in the examples can be used to identify combinations ofbiomarkers that can identify OSCC patients samples and to facilitateassigning the appropriate serum transcriptome-based diagnosis forpatients' specific cancer risk.

Monitoring of profile of salivary mRNA in cell-free fluid phase portionof saliva and/or in other bodily fluid such as blood serum, can be usedin the postoperative management of OSCC patients. It could potentiallybe used for monitoring the efficacy of treatment, or disease recurrenceafter therapy has concluded. Salivary mRNAs and in particular IL8 mayalso serve as prognostic indicators to direct the treatment of patientswith oral cavity cancer. In perspective, high-risk patients can bedirected to more aggressive or adjuvant treatment regimens.

The use of these biomarkers may also improve the staging of the tumor.With traditional techniques, the presence of microscopic distant diseaseis often under recognized. In recent years, there has been a shift fromlocoregional failure to distant failure for patients treated forpresumed locoregional disease.[18] This in part is a reflection ofsubclinical distant disease present prior to the initiation of therapy.Testing for the presence of biomarkers may allow the detection of smallamounts of tumor cells in a background of normal tissue. Salivary mRNAsas biomarkers specific for head and neck tumors or a panel of suchbiomarkers may allow the detection of distant microscopic disease. Fororal cancer, one of the most important applications of the STD approachin this respect is to detect the cancer conversion of oral premalignantlesions.

Profiling of salivary mRNAs can also be used to investigate the role ofgenes in the development of cancer, in particular whether the aberrantexpressions of these genes functionally contribute to the development ofhuman OSCC. The biological significance of differential expression ofthese genes in head and neck/oral cancer should be determined.Identification of cancer-associated genes that are consistently changedin cancer patients will provide us not only with diagnostic markers butalso with insights about molecular profiles involved in head and neckcancer development. Understanding the profile of molecular changes inany particular cancer will be extremely useful because it will becomepossible to correlate the resulting phenotype of that cancer withmolecular events.

Kits of parts associated with the methods herein disclosed are alsodisclosed. In an exemplary embodiment, a kit comprises: a identifier ofa biomarker in a bodily fluid, such as a salivary mRNA or protein, andserum mRNA or protein, the biomarker selected from the group consistingof IL8 IL1B, DUSP1, H3F3A, OAZ1, S100P, SAT, IL6, H3F3A, TPT1, FTH1,NCOA4 and ARCR; and a detector for the identifier, the identifier andthe detector to be used in detecting the bodily fluid profile of thebiomarker of one the methods herein disclosed, wherein the identifier isassociated to the biomarker in the bodily fluid, and the detector isused to detect the identifier, the identifier and the detector therebyenabling the detection of the bodily fluid profile of the biomarker.

The bodily fluid can be saliva, with the detection performed in thecell-free fluid phase portion thereof, or another bodily fluid such asblood serum.

The identifier and the detector able to detect the identifier, areidentifiable by a person skilled in the art. Other compositions and/orcomponents that may be suitably included in the kit and are alsoidentifiable by a person skilled in the art.

The identifier and the reagent can be included in one or morecompositions where the identifier and/or the reagent are included with asuitable vehicle, carrier or auxiliary agent.

In the diagnostic kits herein disclosed, the agents and identifierreagents can be provided in the kits, with suitable instructions andother necessary reagents, in order to perform the methods heredisclosed. The kit will normally contain the compositions in separatecontainers. Instructions, for example written or audio instructions, onpaper or electronic support such as tapes or CD-ROMs, for carrying outthe assay, will usually be included in the kit. The kit can alsocontain, depending on the particular method used, other packagedreagents and materials (i.e. wash buffers and the like).

Further details concerning the identification of the suitable carrieragent or auxiliary agent of the compositions, and generallymanufacturing and packaging of the kit, can be identified by the personskilled in the art upon reading of the present disclosure.

The kit of parts herein disclosed can be used in particular fordiagnostic purpose. As a result a non-invasive diagnostic detection ofpathologies, diseases or disorder and in particular of oral cavity andoropharyngeal cancer in patients, is disclosed.

The use of the fluid phase of saliva has unique advantages over the useof exfoliated cells. Depending on the location of the tumor, one may notbe able to easily access and swab the tumor bed. Although salivarybiomarkers could not identify the site from which the tumor originated,they could identify patients at risk. Such a saliva test could beadministered by nonspecialists in remote locations as a screening toolto select patients for referral for careful evaluation of the upperaerodigestive tract. Finding early stage, previously undetected diseasemay ultimately save lives. Moreover, the use of easily accessiblebiomarkers may prove highly beneficial in large populations orchemoprevention trials. This could be envisioned during routine dentalvisits or targeted screening of individuals at high risk of developmentof the disease. A home test kit can also be envisioned.

Also the use of blood test is envisioned in particular for cancer earlydetection. Recovering the cell-free circulating mRNA or proteinbiomarkers in the serum of cancer patients representing characteristicsof tumor genetic alteration, such as IL6 mRNA and protein, H3F3A, mRNATPT1 mRNA, FTH1 mRNA, NCOA4 mRNA and ARCR mRNA diagnostic for OSCC,could be envisioned as a screening test for presence of occult OSCCduring routine physician's visit with blood work or targeted screeningof individuals at high risk for oral cancer development. A home test kitcan also be envisioned, including preferably

In particular, peripheral blood can be obtained from subjects usingroutine clinical procedures, and mRNA and proteins can be isolated,preferably with an optimized procedures herein disclosed. Real timequantitative PCR and ELISA for the respective cytokine will be performedfor one or biomarkers, such as IL6.

A perspective embodiments of the methods herein disclosed are directedtowards the eventual creation of micro-/nano-electrical mechanicalsystems (MEMS/NEMS) for the ultrasensitive detection of molecularbiomarkers in oral fluid. RNA and protein expression for the validatedOSCC biomarkers will be selected as targets for cancer detection. Theintegration of these detection systems for the concurrent detection ofmRNA and protein for multiple OSCC biomarkers will result in anefficient, automated, affordable system for oral fluid based cancerdiagnostics.

Further details concerning reagents, conditions, compositions techniquesto be used in the method and kits of the disclosure are identifiable bya person skilled in the art upon reading of the present disclosure.

Also appropriate modifications of the STD methods and kits hereindisclosed and exemplified as associated to OSCC and/or HSNCC, for themRNA profiling and transcriptome analysis associated with investigationand diagnosis of other pathology diseases and disorders can be made by aperson skilled in the art upon reading of the present disclosure.

The following examples are provided to describe the invention in furtherdetail. These examples, which set forth a preferred mode presentlycontemplated for carrying out the invention, are intended to illustrateand not to limit the invention

EXAMPLES Example 1: RNA Isolation, Amplification and Gene ExpressionProfiling from Cell-Free Saliva of Normal Donors Normal Subjects

Saliva samples were obtained from ten normal donors from the Division ofOtolaryngology, Head and Neck Surgery, at the Medical Center, Universityof California, Los Angeles (UCLA), CA, in accordance with a protocolapproved by the UCLA Institutional Review Board. The following inclusioncriteria were used: age 30 years; no history of malignancy,immunodeficiency, autoimmune disorders, hepatitis, HIV infection orsmoking. The study population was composed of 6 males and 4 females,with an average age of 42 years (range from 32 to 55 years).

Saliva Collection and Processing to Obtain the Relevant Fluid Phase

Unstimulated saliva were collected between 9 am and 10 am in accordancewith published protocols [38]. Subjects were asked to refrain fromeating, drinking, smoking or oral hygiene procedures for at least onehour prior to saliva collection. Saliva samples were centrifuged at2,600×g for 15 min at 4° C. Saliva supernatant was separated from thecellular phase. RNase inhibitor (Superase-In, Ambion Inc., Austin, Tex.,USA) and protease inhibitor (Aprotinin, Sigma, St. Louis, Mo., USA) werethen added into the cell-free saliva supernatant.

RNA Isolation from Cell-Free Saliva

RNA was isolated from cell-free saliva supernatant using the modifiedprotocol from the manufacturer (QIAamp Viral RNA kit, Qiagen, valencia,CA, USA). Saliva (560 μl), mixed well with AVL buffer (2,240 μl), wasincubated at room temperature for 10 min. Absolute ethanol (2,240 μl)was added and the solution passed through silica columns bycentrifugation at 6,000×g for 1 min. The columns were then washed twice,centrifuged at 20,000×g for 2 min, and eluted with 30 μl RNase freewater at 9,000×g for 2 min. Aliquots of RNA were treated with RNase-freeDNase (DNase I-DNA-free, Ambion Inc., Austin, Tex., USA) according tothe manufacturer's instructions.

The stability of the isolated RNA was examined by RT-PCR typing foractin-β (ACTB) after storage for 1, 3, and 6 months. The resultsreported on FIG. 1A show that the mRNA isolated could be preservedwithout significant degradation for more than 6 month at −80° C.

The quality of isolated RNA was examined by RT-PCR for threehouse-keeping gene transcripts: glyceraldehyde-3-phosphate dehydrogenase(GAPDH), actin-β (ACTB) and ribosomal protein S9 (RPS9). Primers weredesigned using PRIMER3 software (http://www.genome.wi.mit.edu) and weresynthesized commercially (Fisher Scientific, Tustin, Calif., USA) asfollows: the primers having the sequence reported in the attachedsequence listing as SEQ ID NO: 1 and SEQ ID NO: 2 for GAPDH; the primershaving the sequence reported in the attached sequence listing as SEQ IDNO: 3 and SEQ ID NO: 4 for ACTB; the primers having the sequencereported in the attached sequence listing as SEQ ID NO: 5 and SEQ ID NO:6 for RPS9. The quantity of RNA was estimated using Ribogreen® RNAQuantitation Kit (Molecular Probes, Eugene, Oreg., USA). The results areshown in FIG. 1B, wherein GAPDH (B1), RPS9 (B2) and ACTB (B3) weredetected consistently in all 10 cases tested, demonstrating that all 10saliva samples contain mRNAs that encode for house keeping genes: GAPDH,ACTB and RPS9.

The mRNA of these genes could be preserved without significantdegradation for more than 6 months at −80° C., (see results for ACTBreported on FIG. 1A).

Target CRNA Preparation

Isolated RNA was then subjected to linear amplification according topublished method from our laboratory (Ohyama et al., 2000). In brief,reverse transcription using T7-oligo-(dT)₂₄ (SEQ ID NO: 53) as theprimer was performed to synthesize the first strand cDNA. The firstround of in vitro transcription (IVT) was carried out using T7 RNApolymerase (Ambion Inc., Austin, Tex., USA). The BioArray™ High YieldRNA Transcript Labeling System (Enzo Life Sciences, Farmingdale, N.Y.,USA) was used for the second round IVT to biotinylate the cRNA product;the labeled cRNA was purified using GeneChip® Sample Cleanup Module(Affymetrix, Santa Clara, Calif., USA).

The quantity and quality of cRNA were determined by spectrophotometryand gel electrophoresis. Exemplary results of agarose gelelectrophoresis test reported on FIG. 2A show different quantities ofamplified cRNA at the different stages of the RNA amplification.

Also small aliquots from each of the isolation and amplification stepswere used to assess the quality by RT-PCR. Exemplary results reported inFIG. 2B show PCR typing ACTB performed at the various stages of RNAamplification, wherein the expected single band (153 bp) can be detectedin every main step of the salivary RNA amplification process.

The quality of the fragmented cRNA (prepared as described by Kelly,2002) was also assessed by capillary electrophoresis using the 2100Bioanalyzer (Agilent Technologies, Palo Alto, Calif., USA). Exemplaryresults reported in FIG. 2C show one single peak in a narrow range(50-200 bp) demonstrating proper fragmentation.

Gene Expression Profiling in the Targeted cRNA Preparation

Gene expression profiling was performed in cell free-saliva obtainedfrom ten normal donors, wherein on average, 60.5±13.1 ng (n=10) of totalRNA was obtained from 560 μl cell-free saliva samples. The results arereported on Table 1.

TABLE 1 Subject Gender Age RNA (ng)a cRNA (~tg)′~ Present Probesc Probe~%″ 1 F 53 60.4 44.3 3172 14.24 2 M 42 51.6 40.8 2591 11.62 3 M 55 43.234.8 2385 10.70 4 M 42 48.2 38.0 2701 12.12 5 M 46 60.6 42.7 3644 16.356 M 48 64.8 41.8 2972 13.34 7 F 40 75.0 44.3 2815 12.63 8 M 33 77.8 49.34159 18.66 9 F 32 48.8 41.4 2711 12.17 10 F 32 79.8 44.4 4282 19.22 Mean± SD 42 ± 8.3 60.5 ± 13.12 42.2 ± 3.94 3143 ± 665.0 14.11 ± 2.98

The total RNA quantity is the RNA in 560p.L cell-free salivasupernatant; the cRNA quantity is after two rounds of T7 amplification.Number of probes showing present call on HG U133A microarray (detectionp<0.04). Present percentage (P %)=Number of probes assigned presentcall/Number of total probes (22,283 for HG U133A microarray).

After two rounds of T7 RNA linear amplification, the average yield ofbiotinylated cRNA was 42.2±3.9 μg with A260/280=2.067±0.082 (Table 1).The cRNA ranged from 200 bp to 4 kb before fragmentation; and wasconcentrated to approximately 100 bp after fragmentation. The quality ofcRNA probe was confirmed by capillary electrophoresis before thehybridizations. ACTB mRNA was detectable using PCR/RT-PCR on originalsample and products from each amplification steps: first cDNA, first InVitro Transcription (IVT), second cDNA and second IVT, with a resultingagarose electrophoresis pattern comparable to the one shown in FIG. 2B.

Example 2: Microarray Profiling of mRNA from Cell-Free Saliva of NormalDonors

Saliva was collected processed and the RNA isolated as reported inExample 1. Also, stability, quality and quantity of the RNA was assessedare reported in Example 1.

HG-U1331A Microarray Analysis

The Affymetrix Human Genome U133A Array, which contains 22,215 humangene cDNA probe sets representing ˜19,000 genes (i.e., each gene may berepresented by more than one probe sets), was applied for geneexpression profiling. The array data were normalized and analyzed usingMicroarray Suite (MAS) software (Affymetrix). A detection p-value wasobtained for each probe set. Any probe sets with p<0.04 was assigned“present”, indicating the matching gene transcript is reliably detected(Affymetrix, 2001). The total number of present probe sets on each arraywas obtained and the present percentage (P %) of present genes wascalculated. Functional classification was performed on selected genes(present on all ten arrays, p<0.01) by using the Gene Ontology MiningTool (www.netaffx.com).

Salivary mRNA profiles of ten normal subjects were obtained using HGU133A array contains 22,283 cDNA probes. An average of 3,143±665.0 probesets (p<0.04) was found on each array (n=10) with assigned presentcalls. These probe sets represent approximately 3,000 different mRNAs.The average present call percentage was 14.11±2.98% (n=10). A referencedatabase which includes data from the ten arrays was generated. Theprobe sets representing GAPDH, ACTB and RPS9 assigned present calls onall 10 arrays. There were totally 207 probe sets representing 185 genesassigned present calls on all 10 arrays with detection p<0.01. These 10genes were categorized on the basis of their known roles in biologicalprocesses and molecular functions. Biological processes and molecularfunctions of 185 genes in cell-free saliva from ten normal donors (dataobtained by using Gene Ontology Mining Tool) are reported on Table 2.

TABLE 2 Biological process^(a) Genes, nb Molecular function^(a) Genes,nb Cell growth and/or maintenance 119 Binding 118 Metabolism 93 Nucleicacid binding 89 Biosynthesis 70 RNA binding 73 Protein metabolism 76Calcium ion binding 12 Nucleotide metabolism 10 Other binding 23 Othermetabolisms 18 Structural molecule 95 Cell organization and biogenesis 2Ribosomal constituent 73 Homeostasis 3 Cytoskeleton constituent 17 Cellcycle 5 Muscle constituent 2 Cell proliferation 11 Obsolete 15 Transport5 Transporter 4 Cell motility 8 Enzyme 20 Cell communication 34 Signaltransduction 10 Response to external stimulus 19 Transcription regulator7 Cell adhesion 3 Translation regulator 5 Cell-cell signaling 5 Enzymeregulator 9 Signal transduction 17 Cell adhesion molecule 1 Obsolete 8Molecular function unknown 6 Development 18 Death 2 Biological processunknown 11

One gene may have multiple molecular functions or participate indifferent biological processes. Number of genes classified into acertain group/subgroup. The major functions of the 185 genes are relatedto cell growth/maintenance (119 genes), molecular binding (118 genes)and cellular structure composition (95 genes). We termed these 185 genesas “Normal Salivary Core Transcriptome (NSCT)”.

Example 3: Q-PCR Validation and Quantitation Analysis of MicroarrayProfiling from Cell-Free Saliva of Normal Donors

The Microarray analysis performed in Example 2 was validated through aquantitative gene expression analysis by Q-PCR

Quantitative Gene Expression Analysis Q-PCR

Real time quantitative PCR (Q-PCR) was used to validate the presence ofhuman mRNA in saliva by quantifying selected genes from the 185 “NormalSalivary Core Transcriptome” genes detected by the Microarray profilingreported in Example 2. Genes ILA B, SFN and K-ALPHA-1, which wereassigned present calls on all 10 arrays, were randomly selected forvalidation.

Q-PCR was performed using iCycler™ thermal Cycler (Bio-Rad, Hercules,Calif., USA). A 2 μl aliquot of the isolated salivary RNA (withoutamplification) was reverse transcribed into cDNA using MuLV ReverseTranscriptase (Applied Biosystems, Foster City, Calif., USA). Theresulting cDNA (3 μl) was used for PCR amplification using iQ SYBR GreenSupermix (Bio-Rad, Hercules, Calif., USA). The primers were synthesizedby Sigma-Genosys (Woodlands, Tex., USA) as follows: the primers havingthe sequence reported in the attached sequence listing as SEQ ID NO: 7and SEQ ID NO: 8 for interleukin 1, beta (IL B); the primers having thesequence reported in the attached sequence listing as SEQ ID NO:9 andSEQ ID NO: 10 for stratifin (SFN); the primers having the sequencereported in the attached sequence listing as SEQ ID NO: 11 and SEQ IDNO: 12 for tubulin, alpha, ubiquitous (K-ALPHA-1). All reactions wereperformed in triplicate with conditions customized for the specific PCRproducts. The initial amount of cDNA of a particular template wasextrapolated from a standard curve using the LightCycler software 3.0(Bio-Rad, Hercules, Calif., USA). The detailed procedure forquantification by standard curve has been previously described(Ginzinger, 2002).

Q-PCR results showed that mRNA of IL1B, SFN and K-ALPHA-1 weredetectable in all 10 original, unamplified, cell-free saliva. Therelative amounts (in copy number) of these transcripts (n=10) are:8.68×10³±4.15×10³ for IL1B; 1.29×10⁵±1.08×10⁵ for SFN; and4.71×10⁶±8.37×10⁵ for K-ALPHA-1. The relative RNA expression levels ofthese genes measured by Q-PCR were similar to those measured by themicroarrays (data not shown).

Example 4: IL6 and IL8 mRNA Isolation Amplification and Analysis of theExpression in Cell-Free Saliva of OSCC Patients Patients Selection

Patients were recruited from the Division of Head and Neck Surgery atthe University of California, Los Angeles (UCLA) Medical Center, LosAngeles, Calif.; the University of Southern California (USC) MedicalCenter, Los Angeles, Calif.; and the University of California SanFrancisco (UCSF) Medical Center, San Francisco, Calif., over a 6-monthperiod.

Thirty-two patients with documented primary T1 or T2 squamous cellcarcinoma of the oral cavity (OC) or oropharynx (OP) were included inthis study. All patients had recently been diagnosed with primarydisease, and had not received any prior treatment in the form ofchemotherapy, radiotherapy, surgery, or alternative remedies. An equalnumber of age and sex matched subjects with comparable smoking historieswere selected as a control comparison group.

Among the two subject groups, there were no significant differences interms of mean age (standard deviation, SD): OSCC patients, 49.3 (7.5)years; normal subjects, 48.8 (5.7) years (Student's t test P>0.80);gender (Student's t test P>0.90); or smoking history (Student's t testP>0.75). No subjects had a history of prior malignancy,immunodeficiency, autoimmune disorders, hepatitis, or HIV infection.Each of the individuals in the control group underwent a physicalexamination by a head and neck surgeon, to ensure that no suspiciousmucosal lesion was present.

Saliva Collection and Processing

Informed consent had been given by all patients. Saliva and serumprocurement procedures were approved by the institutional review boardat each institution: the University of California, Los Angeles (UCLA);the University of Southern California (USC); and the University ofCalifornia San Francisco (UCSF).

Saliva from 32 patients with OC or OP SCCA, and 32 unaffected age- andgender-matched control subjects were obtained for a prospectivecomparison of cytokine concentration.

The subjects were required to abstain from eating, drinking, smoking, orusing oral hygiene products for at least one hour prior to salivacollection. Saliva collection was performed using the “draining(drooling)” method of Navazesh and Christensen,[7] for a total donationof 5 cc saliva. Saliva samples were subjected to centrifugation at 3500rpm (2600×g) for 15 minutes at 4° C. by a Sorvall RT6000D centrifuge(DuPont, Wilmington, Del.). The fluid-phase was then removed, and RNAse(Superase-In, RNAse Inhibitor, Ambion Inc., Austin, Tex.) and protease(Aprotinin, Sigma, St. Louis, Mo.; Phenylmethylsulfonylfluoride, Sigma,St. Louis, Mo.; Sodium Orthovanadate, Sigma, St. Louis, Mo.) inhibitorswere then added promptly on ice. The conditions for the separation ofthe cellular and fluid phases of saliva were optimized to ensure nomechanical rupture of cellular elements which would contribute to themRNA detected in the fluid phase. All samples were subsequently treatedwith DNAse (DNAseI-DNA-free, Ambion Inc., Austin, Tex.). The cell pelletwas retained and stored at −80° C.

RNA Isolation from Cell-Free Saliva

560 μl of saliva supernatant were then processed using the QIAamp ViralRNA mini kit (QIAGEN, Chatsworth, Calif.) kit. RNA was extractedaccording to the manufacturer's instructions. Samples were air-dried andresuspended in water treated with diethyl pyrocarbonate and were kept onice for immediate usage or stored at −80° C. Aliquots of RNA weretreated with RNAse-free DNAse (DNAseI-DNA-free, Ambion Inc., Austin,Tex.) according to the manufacturer's instructions. Concentrations ofRNA were determined spectrophotometrically, and the integrity waschecked by electrophoresis in agarose gels containing formaldehyde.

Reverse Transcriptase-Polymerase Chain Reaction

Presence of IL6 and IL8 mRNA transcripts in the fluid phase in salivawas tested by using reverse transcriptase-polymerase chain reaction(RT-PCR).

RNA from each sample was reverse-transcribed in 40 μl of reactionmixture containing 2.5 U of Moloney murine leukemia virus reversetranscriptase (Applied Biosystems Inc. (ABI, Foster City, Calif.) and 50pmol of random hexanucleotides (ABI, Foster City, Calif.) at 42° C. for45 minutes. Based on the published sequences, oligonucletide primerswere synthesized commercially at Fisher Scientific (Tustin, Calif.) forPCR as follows: the primers having the sequence reported attachedsequence listing as SEQ ID NO: 13 and SEQ ID NO: 14 for β-actin; theprimers having the sequence reported attached sequence listing as SEQ IDNO: 15 and SEQ ID NO: 16 for IL8; and the primers having the sequencereported attached sequence listing as SEQ ID NO: 17 and SEQ ID NO: 18for IL6.

Amplification of the complementary DNA (cDNA) was carried out using 50cycles at 95° C. for 20 seconds, 60° C. for 30 seconds, and 72° C. for30 seconds; followed by a final extension cycle of 72° C. for 7 minutes.Specificity of the PCR products was verified by the predicted size andby restriction digestion. To establish the specificity of the responses,negative controls were used in which input RNA was omitted or in whichRNA was used but reverse transcriptase omitted. As a positive control,mRNA was extracted from total salivary gland RNA (Human Salivary GlandTotal RNA, Clontech, Palo Alto, Calif.). To ensure RNA quality, allpreparations were subjected to analysis of expression.

The RT-PCR studies so performed showed that saliva and serum containedmRNA encoding for IL6 and IL8. Exemplary results reported in FIG. 3,show PCR products of the sizes (95 bp for IL6 and 88 bp for IL8) thatwere expected from the selected primers. The same-sized products wereexpressed in the positive control.

In order to ensure that the RNA and protein analyzed were from the fluidphase of saliva only and to ensure the lack of contamination byintracellular components, the centrifugation speed for the salivasamples was optimized. PCR for the housekeeping genes p-actin andubiquitin on whole saliva samples, and samples that had been centrifugedat various speeds using DNA as a marker of cell lysis and spillage ofintracellular components. The results support an optimal centrifugationspeed for saliva samples of 2,600±52×g, with a preferred speed of2,600×g (see exemplary results reported on FIG. 4)

Example 5: IL6 and IL8 mRNA Isolation, Amplification and Analysis of theExpression in Serum of OSCC Patients

Patients recruited as reported in Example 4, where subjected to analysisof presence of IL6 and IL8 mRNA in blood serum.

Serum Collection and Processing

Serum from 19 patients with OC or OP SCCA, and 32 unaffected age- andgender-matched control subjects were obtained for a prospectivecomparison of cytokine concentration. Among the subject groups, therewere no significant differences in terms of age, gender, alcoholconsumption, or smoking history (P>0.75).

Blood was drawn from control subjects and patients prior to treatment.Sera were collected by centrifuging whole blood at 3000 rpm (1000×g) for10 minutes at 15° C. by a Sorvall RT6000D centrifuge (DuPont,Wilmington, Del.). Serum was then separated, and RNAse (Superase-In,RNAse Inhibitor, Ambion Inc., Austin, Tex.) and protease (Aprotinin,Sigma, St. Louis, Mo.; Phenylmethylsulfonylfluoride, Sigma, St. Louis,Mo.; Sodium Orthovanadate, Sigma, St. Louis, Mo.) inhibitors were thenadded promptly on ice. All samples were subsequently treated with DNAse(DNAseI-DNA-free, Ambion Inc., Austin, Tex.). The aliquots were storedat −80° C. until further use.

Reverse Transcriptase-Polymerase Chain Reaction

Presence of IL6 and IL8 mRNA transcripts in the serum was tested byusing reverse transcriptase-polymerase chain reaction (RT-PCR) performedas described in Example 4 above.

The RT-PCR studies so performed showed that serum contained mRNAencoding for IL6 and IL8, with electrophoresis gel pattern comparable tothe one shown in FIG. 3.

In order to ensure that the RNA and protein analyzed were from the fluidphase of serum only and to ensure the lack of contamination byintracellular components, the centrifugation speed for the serum sampleswas optimized following the same approach described in Example 4 forsaliva samples. The results support an optimal centrifugation speed forsaliva samples of 1,000±20×g with a preferred speed of 1,000×g.

Example 6: IL6 and IL8 Cytokine Levels Analysis in Saliva from OSCCPatients

On demonstrating that IL6 and IL8 mRNA transcripts were present in thefluid phase in saliva, we prospectively examined and compared the levelsof IL6 and IL8 in the saliva of unaffected subjects and patients withOSCC using quantitative real time PCR (qRT-PCR) and ELISA.

Saliva from 32 patients with OSCC, and 32 age- and gender-matchedcontrol subjects were obtained. Among the subject groups, there were nosignificant differences in terms of age, gender, alcohol consumption, orsmoking history (P>0.75).

Real Time PCR for Quantification of IL6 and IL8 mRNA Concentrations inSaliva from Patients and Normal Subjects

To analyze quantitatively the result of RT-PCR, quantitative real-timePCR (Bio-Rad iCycler, Thermal Cycler, Bio-Rad Laboratories, Hercules,Calif.) was used. Each sample was tested in triplicate. Theamplification reactions were carried out in a 20 μl mixture, using iQSYBR Green Supermix (Bio-Rad Laboratories, Hercules, Calif.). Afterinitial denaturation at 95° C. for 3 minutes, 50 PCR cycles wereperformed at 60° C. for 20 seconds, then 20 seconds at 72° C., then 20seconds at 83° C., followed by 1 minute at 95° C., then followed by afinal 1 minute extension at 55° C. Aliquots were taken from each welland checked by electrophoresis in agarose gels in order to ensure thespecificity of the products.

The RT-PCR results are illustrated by the diagram shown in FIG. 5A. Suchresults show that IL8 at both the mRNA and protein levels, was detectedin higher concentrations in the saliva of patients with OSCC whencompared with control subjects (l test, P<0.01). There was a significantdifference in the amount of IL8 mRNA expression between saliva from OSCCpatients and disease-free controls. The mean copy number was 1.1×10³ forthe OSCC group, and 2.6×10¹ for the control group. The differencebetween the two groups was highly statistically significant (P<0.0008).

No significant differences were instead found in the salivaryconcentration of IL6 at the mRNA level. Within the sample size studies,the inventors were also unable to detect differences between smoking andnonsmoking subjects.

ELISA for Quantification of IL6 and IL8 Protein Concentrations in Salivafrom Patients and Normal Subjects

ELISA kits for IL6 and IL8 were used (Pierce Endogen, Rockford, Ill.)according to the manufacturer's protocol. Each sample was tested induplicate in each of two replicate experiments. After development of thecolorimetric reaction, the absorbance at 450 nm was quantitated by aneight channel spectrophotometer (EL800 Universal Microplate Reader,BIO-TEK Instruments Inc., Winooski, Vt.), and the absorbance readingswere converted to pg/ml based upon standard curves obtained withrecombinant cytokine in each assay. If the absorbance readings exceededthe linear range of the standard curves, ELISA assay was repeated afterserial dilution of the supernatants. Each sample was tested in at leasttwo ELISA experiments, and the data were calculated from the mean oftests for each sample.

The ELISA findings are illustrated by the diagram shown in FIG. 5B. Thelevels of IL8 in the saliva of OSCC patients were significantly higher(720 pg/dL) than those in the saliva of the control group (250 pg/dL)(P<0.0001). To ensure that the elevated levels of IL8 protein in salivawere not due to an elevation of total protein levels in the saliva ofOSCC patients, we compared the total protein concentrations in salivaamong the two groups. No significant differences were found (P>0.05).

No significant differences were found in the salivary concentration ofIL6 at the protein level. Also in the ELISA analysis, no differenceswere detected within the sample size studies between smoking andnonsmoking subjects.

Example 7: IL6 and IL8 Cytokine Levels Analysis in Serum from OSCCPatients

We also examined and compared the levels of IL6 and IL8 in the serum ofunaffected subjects and patients with OSCC using qRT-PCR and ELISA. Thepatients were selected as described in Example 4 and the serum processedas described in Example 5.

Real Time PCR for Quantification of IL6 and IL8 mRNA Concentrations inSerum from Patients and Normal Subjects

To analyze quantitatively the result of RT-PCR, quantitative real-timePCR was performed as described in Example 6.

The RT-PCR results are illustrated by the diagram shown in FIG. 6A. Suchresults show that IL6 at mRNA level was detected in higherconcentrations in the serum of patients with OSCC when compared withcontrol subjects (t test, P<0.001). We noted a significant difference inthe amount of IL6 mRNA expression between serum from OSCC patients anddisease-free controls. The mean copy number was 5.2×10⁴ for the OSCCgroup, and 3.3×10³ for the control group. The difference between the twogroups was highly statistically significant (P<0.0004).

No significant differences were instead found in the serum concentrationof IL8 at the mRNA level. Within the sample size studies, the inventorswere also unable to detect differences between smoking and nonsmokingsubjects.

ELISA for Quantification of IL6 and IL8 Protein Concentrations in Serumfrom Patients and Normal Subjects

ELISA tests for quantification of IL6 and IL8 protein concentrations inserum were performed as described in Example 6.

The relevant ELISA findings are illustrated by the diagram shown in FIG.6B. The mean levels of IL6 in the serum of OSCC patients weresignificantly higher (87 pg/dL) than those in the serum of the controlgroup (0 pg/dL) (P<0.0001).

No significant differences were found in the serum concentration of IL8at the protein level. Also in the ELISA analysis, no differences weredetected within the sample size studies between smoking and nonsmokingsubjects.

Example 8: ROC and Sensitivity/Specificity Analysis

Statistical analysis of the data collected in outcome of the experimentsreported on Examples 1 to 7 above demonstrates the specificity andsensitivity of these biomarkers for HNSCC, and their predictive value.

Statistical Analysis

The distributions of patient demographics were calculated overall andseparately for OSCC cases and controls, and were compared between thetwo arms with either the Student's t-test for continuous measures ortwo-by-two Chi-square tables for categorical measures. The distributionsof IL6 and IL8 levels in saliva and serum were computed and comparedbetween the OSCC cases and controls using two independent group t-tests.Differences were considered significant for P values less than 0.01. Dueto the range of the IL6 and IL8 levels, log transformations of thesemeasures were also used in the analyses. Data were expressed as themean±SD. Age, gender, and smoking history were controlled at the grouplevel in the experimental design; these patient factors were alsoadjusted in the analyses when comparing IL6 and IL8 through regressionmodeling.

Using the binary outcome of the disease (OSCC cases) and non-disease(controls) as dependent variables, logistic regression models werefitted to estimate the probability of developing OSCC as a function ofeach of the potential biomarkers (IL6 or IL8), controlling for patientage, gender, and smoking history. Using the fitted logistic models,receiver operating characteristic (ROC) curve analyses were conducted toevaluate the predictive power of each of the biomarkers[8][9][10].Through the ROC analyses, we calculated sensitivities and specificitiesby varying the criterion of positivity from the least (cut atprobability of 0) to the most stringent (cut at probability of 1). Theoptimal sensitivity and specificity was determined for each of thebiomarkers, and the corresponding cutoff/threshold value of each of thebiomarkers was identified. The biomarker that has the largest area underthe ROC curve was identified as having the strongest predictive powerfor detecting OSCC.

Clinical Data

The mean (SD) age of the patients with OSCC was 49.3 (7.5) years (range,42-67 years) vs. 48.8 (5.7) years (range, 40-65 years) in the controlgroup; (Student's t test P>0.80). Among the two subject groups, therewere no significant differences in terms of age (mean age): OSCCpatients, 49.3 years; normal subjects, 48.8 years (Student's t testP>0.80); gender (Student's t test P>0.90); or smoking history (Student'st test P>0.75).

ROC (Receiver Operating Characteristic) curves, plots of sensitivitiesversus 1-specificities, were generated for each of the potentialbiomarkers. Age, gender, and smoking history were controlled asdescribed above. The areas under the ROC curves were calculated, asmeasures of the utility of each biomarker for detecting OSCC.

FIG. 7A and FIG. 7B show the ROC curves for IL8 in saliva and IL6 inserum, respectively. The calculated ROC values (for predicting OSCC)were 0.978 for IL8 in saliva, and 0.824 for IL6 in serum. Based on thedistribution of sensitivities and specificities, thresholds ofbiomarkers were chosen for detecting OSCC. Based upon our data, for IL8in saliva, a threshold value of 600 pg/dL yields a sensitivity of 86%and a specificity of 97%. Similarly, for IL6 in serum, a threshold valueof greater than 0 pg/dL yields a sensitivity of 64% and a specificity of81%.

The combination of biomarkers: IL-8 in saliva and IL-6 in serum holdsgreat potential for OSCC diagnostics as ROC analysis yields asensitivity of 99% and a specificity of 90% as shown in FIG. 7C.

The detailed statistics of the area under the ROC curves, the thresholdvalues, and the corresponding sensitivities and specificities for eachof the potential biomarkers in saliva and in serum are listed in Table3.

The detailed statistics of the area under the ROC curves, the thresholdvalues, and the corresponding sensitivities and specificities for eachof the potential biomarkers in saliva and in serum are listed in Table 3below.

TABLE 3 Area under Threshold/ Biomarker ROC Cutoff SensitivitySpecificity IL8 saliva protein 0.978 600 pg/mL 86% 97% IL6 serum0.824 >0 pg/mL 57% 100%  protein IL8 saliva protein 0.994 >600 pg/ml 99%90% & IL6 serum >0 p/ml protein

Example 9: RNA Isolation, Amplification and Gene Expression Profilingfrom Serum of Oscc Patients Subject Selection

Thirty-two OSCC patients were recruited from Medical Centers atUniversity of California, Los Angeles (UCLA) and University of SouthernCalifornia (USC), Los Angeles, Calif. All patients had recently beendiagnosed with primary T1/T2 OSCC, and had not received any priortreatment in the form of chemotherapy, radiotherapy, surgery, oralternative remedies. Thirty-five normal donors were recruited ascontrols from the general population at School of Dentistry, UCLA. Nosubjects had a history of prior malignancy, immunodeficiency, autoimmunedisorders, hepatitis, or HIV infection. All subjects signed theInstitutional Review Board approved consent form agreeing to serve asblood donors for this study.

Totally sixty-seven subjects were recruited, including 32 OSCC patientsand 35 normal subjects. Among the two subject groups, there were nosignificant differences in terms of mean age (standard deviation, SD):OSCC patients, 49.3 (7.5) years; normal subjects, 47.8 (6.4) years(Student's t test P=0.84). The gender distribution in OSCC group was10:22 (female number/male number) and in control group was 14:21(Chi-square test P=1). We matched the smoking history of these twogroups by determining the follows. All subjects were asked: (1) For howmany years had they, smoked?(2) How many packs per day had they smoked?(3) How many years had elapsed since they had quit smoking (if they hadindeed quit)? (4) Did they only smoke cigarettes, or did they also usecigars, pipes, chewing tobacco, or marijuana? We then optimized thematch between patients and controls in terms of the above: (1) similarpack-year history (2) similar time lapse since they had quit smoking (3)use of cigarettes exclusively. There was no significant differencebetween two groups in the smoking history (Student's t test P=0.77).

Blood Collection and Processing.

Blood procurement procedure was approved by the institutional reviewboard at UCLA and USC. Blood was drawn from control subjects andpatients prior to treatment. The whole blood then underwent acentrifugation by 1,000×g for 10 minutes at 15° C. by a Sorvall RT6000Dcentrifuge (DuPont, Wilmington, Del.). Serum was then separated, and 100U/mL RNase inhibitor (Superase-In, Ambion Inc., Austin, Tex.) was addedpromptly to the serum. The aliquots were stored at −80° C. until furtheruse.

RNA Isolation from Serum.

RNA was isolated from 560 μl serum using QIAamp Viral RNA kit (Qiagen,Valencia, Calif.). Aliquots of isolated RNA were treated with RNase-freeDNase (DNasel-DNA-free, Ambion Inc., Austin, Tex.) according to themanufacturer's instructions. The quality of isolated RNA was examined byRT-PCR for four housekeeping gene transcripts: β-actin (ACTB),β-2-microglobulin (B2M), glyceraldehyde-3-phosphate dehydrogenase(GAPDH), and ribosomal protein S9 (RPS9). Based on the publishedsequences, oligonucletide primers were designed and then synthesized(Sigma Genosis, Woodlands, Tex.) for PCR. RT-PCR was performed toamplify the mRNAs' coding region phenotyped in 3 segments using a commonupstream primer and three different downstream primers selected from thefour housekeeping gene transcripts for RT-PCR shown in Table 4.

TABLE 4 Accession no. Full length Amplicon Name (NCBI) (bp) Primersequences (bp) ACTB X00351 1761 F: SEQ ID NO: 19 195 R1: SEQ ID NO: 20705 R2: SEQ ID NO: 21 1000 R3: SEQ ID NO: 22 B2M NM_004048 987 F: SEQ IDNO: 23 216 R1: SEQ ID NO: 24 591 R2: SEQ ID NO: 25 848 R3: SEQ ID NO: 26GAPDH M33197 1268 F: SEQ ID NO: 27 140 R1:SEQ ID NO: 28 755 R2: SEQ IDNO: 29 1184 R3: SEQ ID NO: 30 RPS9 NM_001013 692 F: SEQ ID NO: 31 188R1: SEQ ID NO: 32 426 R2: SEQ ID NO: 33 614 R3: SEQ ID NO: 34

In particular four serum human mRNAs were selected and coding regionphenotyped in 3 segments using a common upstream primer and threedifferent downstream primers dividing the coding region approximatelyinto three parts. 10 μl of each PCR reaction was electrophoresed on a 2%agarose gel and stained with EtBr.

Specificity of all the PCR products was verified by the predicted sizecomparing the positive control (Human Salivary Gland Total RNA,Clontech, Palo Alto, Calif.). Negative controls were used in which inputRNA was omitted or in which RNA was used but reverse transcriptaseomitted.

The serum phenotype of mRNA product from human was evaluated by RT-PCRand electrophoresis. Exemplary results reported in FIG. 8, showedtranscripts from four housekeeping genes (ACTB, B2M, GAPDH, and RPS9)could be detected. In particular, amplicons for RPS9 with sizes of 188,426 and 614 bp were detected (see FIG. 8 lane 2, 3 and 4 respectively);amplicons for GAPDH with sizes of 140,755 and 1,184 bp were detected(see FIG. 8 lane 5, 6 and 7 respectively); amplicons for B2M with sizesof 216,591 and 848 bp were detected (see FIG. 8 lane 8, 9 and 10respectively); and amplicons for ACTB with sizes of 195,705 and 1,000 bpwere detected (see FIG. 8 lane 11, 12 and 13 respectively). Controlswere performed even if controls data are not shown in the Figure.

The longest PCR products we amplified covered 56.8% (ACTB), 85.9% (B2M),93.4% (GAPDH) and 88.9% (RPS9) of the full length of the correspondingmRNAs, according to the NCBI GenBank database. This result alsoindicated there could be intact human mRNA circulating in blood in acell-free form.

Example 10: Microarray Profiling of mRNA of Serum from OSCC Patients

Serum from ten OSCC patients (8 male, 2 female, age=51±9.0) and from tengender and age matched normal donors (age=49±5.6) was collected andprocessed as reported in Example 9 for use in microarray analysis.

Microarray Analysis

Isolated RNA from serum was subjected to linear amplification byRiboAmp™ RNA Amplification kit (Arcturus, Mountain View, Calif.).Following previously reported protocols [55], the Affymetrix HumanGenome U133A Array, which contains 22,215 human gene cDNA probe setsrepresenting ˜19,000 genes (i.e., each gene may be represented by morethan one probe sets), was applied for gene expression profiling.

The raw data were imported into DNA-Chip Analyzer 1.3 (dChip) softwarefor normalization and model-based analysis [60]. dChip gives theexpression index which represents the amount of mRNA/Gene expression andanother parameter, called the present call of, whether or not the mRNAtranscript was actually present in the sample (14). S-plus 6.0(Insightful, Seattle, Wash.) was used for all statistical tests.

Three criteria were used to determine differentially expressed genesbetween OSCCs and controls. First, genes that were assigned as “absent”call in all samples were excluded. Second, a two-tailed student's t testwas used for comparison of average gene expression levels among theOSCCs (n=10) and controls (n=10). The critical alpha level of 0.05 wasdefined for statistical significance. Third, fold ratios were calculatedfor those genes that showed statistically significant difference(P<0.05). Only those genes that exhibit at least 2-fold change will beincluded for further analysis.

The HG U133A microarrays were used to identify the difference insalivary RNA profiles between cancer patients and matched normalsubjects. Among the 14,268 genes included by the previously describedcriteria, we identified 335 genes with P value less than 0.05 and a foldchange 22. Among these genes, there are 223 up-regulated genes and 112down-regulated genes in the OSCC group. According to Affymetrix, a genethat was assigned with a present call indicates this gene is reliablydetected in the original sample. The number of genes that were assignedpresent and the present percentage on each array were shown in Table 5reporting the human mRNA expression profiling in serum.

TABLE 5 Normal OSCC Present Probe Present Probe Subject Gender AgeProbes^(a) P %^(b) Gender Age Probes^(a) P %^(b) 1 F 53 1564 7.02 F 551990 6.93 2 M 55 1600 7.16 M 61 2924 13.12 3 M 42 1600 7.18 M 42 21269.54 4 M 46 1716 7.7 M 46 3316 14.88 5 M 42 1845 8.26 M 42 2937 13.18 6M 54 1854 8.32 M 52 1794 8.05 7 F 51 1903 8.54 F 67 2119 9.51 8 M 482032 9.12 M 46 2019 9.06 9 M 56 1823 8.18 M 61 4646 20.85 10 M 42 19798.68 M 44 2362 10.6 Mean ± SD 49 ± 5.6 1792 ± 165 6.04 ± 0.74 51 ± 9.02623 ± 868* 11.8 ± 3.90 ^(a)Number of probes showing present call on HGU133A microarray (detection P < 0.04). ^(b)Present percentage (P %) =Number of probes assigned present call/Number of total probes (22,283for HG U133A microarray). *The arrays for OSCC have significant moreprobes assigned with present call than those for control group (P ≤0.002, Wilcoxon test).

On average, there are 2623±868 probes in OSCC arrays and 1792±165 probesin control arrays that were assigned with present calls. OSCC group havesignificant more present probes than control group (P≤0.002, Wilcoxontest).

Using a more stringent criterion that, for a certain gene, the presentcall was assigned consistently to all arrays among all cancers (n=10) orall controls (n=10), we identified 62 genes to be the candidates forfurther analysis. We noted that these 62 genes are all up-regulated inOSCC serum, whereas there are no genes found down-regulated using thesame filtering criteria.

Example 11: Q-PCR Validation and Quantitation Analysis of MicroarrayProfiling from Cell-Free Saliva of OSCC Patients

qPCR was performed to quantify a subset of differently expressedtranscripts in saliva and to validate the microarray findings of Example10, on an enlarged sample size including saliva from 32 OSCC patientsand 35 controls.

Quantitative PCR (qPCR) Assay.

Primer sets were designed by using PRIMERS software (Table 2). UsingMuLV reverse transcriptase (Applied Biosystems, Foster City, Calif.) andrandom hexamers as primer (ABI, Foster City, Calif.), cDNA wassynthesized from the original and un-amplified serum RNA. The qPCRreactions were performed in an iCycler™ iQ real-time PCR detectionsystem (Bio-Rad, Hercules, Calif., USA), using iQ SYBR Green Supermix(Bio-Rad, Hercules, Calif.). All reactions were performed in triplicatewith customized conditions for specific products. The relative amount ofcDNA/RNA of a particular template was extrapolated from the standardcurve using the LightCycler software 3.0 (Bio-Rad, Hercules, Calif.,USA). A two-tailed student's t test was used for statistical analysis.

Ten significant up-regulated genes: H3F3A, TPT1, FTH1, NCOA4, ARCR,THSMB (Thymosin beta 10), PRKCBI (Protein Kinase C, beta 1), FTL1(Ferritin Light polypeptide), COX4I1 (Cytochrome c oxidase subunit IVisoform 1) and SERP1 (stress associated endoplasmic reticulum protein 1;ribosome associated membrane protein 4) were selected based on theirreported cancer-association as shown in Table 6, reporting ten genesselected for qPCR validation.

TABLE 6 Accession qPCR Probe set ID No. P (t (HG U133A) Gene name Symbol(NCBI) test) 211940_x_at H3 histone, family 3A H3F3A BE869922 0.003211943_x_at Tumor protein, TPT1 AL565449 0.005 translationally-controlled 1 200748_s_at Ferritin, heavy FTH1 NM_002032 0.008polypeptide 1 210774_s_at Nuclear receptor NCOA4 AL162047 0.021coactivator 4 200059_s_at Ras homolog gene ARCR BC001360 0.048 family,member A 217733_s_at Thymosin, beta 10 THSMB NM_021103 0.318 209685_s_atProtein kinase C, PRKCB1 M13975 0.615 beta 1 208755_x_at Ferritin, lightFTL1 BF312331 0.651 polypeptide 200086_s_at Cytochrome c oxidase COX4I1AA854966 0.688 subunit IV isoform 1 200971_s_at Stress-associated SERP1NM_014445 0.868 endoplasmic reticulum protein 1; ribosome associatedmembrane protein 4

Table 6 presents their quantitative alterations in serum from OSCCpatients, determined by qPCR. The results confirmed that transcripts ofH3F3A, TPT1, FTH1, NCOA4 and ARCR were significantly elevated in thesaliva of OSCC patient (Wilcoxon Signed Rank test, P<0.05). We did notdetect the statistically significant differences in the amount of theother five transcripts by qPCR.

Example 12: ROC and Sensitivity/Specificity Analysis

Statistical analysis of the data collected in outcome of the experimentsreported on Examples 9 to 11 above demonstrates the specificity andsensitivity of these biomarkers for H NSCC, and their predictive value.

Receiver Operating Characteristic Curve Analysis and Prediction Models.

Utilizing the qPCR results, multivariate classification models wereconstructed to determine the best combination of the selected serumtranscripts for cancer prediction. Firstly, using the binary outcome ofthe disease (OSCC) and non-disease (normal) as dependent variables, alogistic regression model was constructed [61]. Age, gender and smokinghistory are controlled in the data collection procedure.

Leave-one out cross validation was used to validate the logisticregression model. The cross validation strategy first removes oneobservation and then fits a logistic regression model from the remainingcases using all markers. Stepwise model selection is used for each ofthese models to remove variables that do not improve the model.Subsequently, the observing values for the case that was left out wereused to compute a predicted class for that observation. The crossvalidation error rate is then the number of samples predictedincorrectly divided by the number of samples.

The Receiver operating characteristic (ROC) curve analysis was thencomputed for the best final logistic model (S-plus 6.0), using thefitted probabilities from the model as possible cut-points forcomputation of sensitivity and specificity. Area under the curve wascomputed via numerical integration of the ROC curve.

To demonstrate the utility of circulating mRNAs in serum for OSCCdiscrimination, two classification/prediction models were observed.Using the qPCR data, a logistic regression model was built compose ofsix serum transcripts previously examined, ARHA, FTH1, H3F3A, TPT1,COX4I1 and FTL1. Those six transcripts in combination provided the bestprediction, which was then validated by the leaving-one-out validation.Out of 67 leaving-one-out trial, 54 (81%) of the best logistic modelswas found to the same model as the one from the whole data and thevalidation error rate was 31.3% (21/67).

Results are reported in FIG. 9, wherein the ROC curve computed for thislogistic regression model is shown.

Using a cut-off probability of 44% a sensitivity of 84% and aspecificity of 83% were obtained. The final model predicts correctly for56 (83.5%) subjects out of 67 with 0.84 (27/32) sensitivity and 0.83(29/35) specificity and it misclassifies 6 subjects for control and 5for OSCC. The calculated area under the ROC curve was 0.88 for thislogistic regression model.

Tree-Based Classification Model. Classification and Regression Tree(CART),

Secondly, another prediction model utilizing the qPCR results was builtby a tree-based classification method. The classification and regressiontrees (CART), was constructed by S-plus 6.0 using the serum transcriptsas predictors from qPCR result. CART fits the classification model bybinary recursive partitioning, where each step involves searching forthe predictor variable that results in the best split of the cancerversus the normal groups [62]. CART used the entropy function withsplitting criteria determined by default settings for S-plus. By thisapproach, the parent group containing the entire samples (n=67) wassubsequently divided into cancer groups and normal groups. Our initialtree was pruned to remove all splits that did not result in sub-brancheswith different classifications.

A second model, the “classification and regression trees (CART) model”,was generated according to the diagram reported in FIG. 10.

Our fitted CART model used the serum mRNA concentrations of THSMB andFTH1 as predictor variables for OSCC. THSMB, chosen as the initialsplit, with a threshold of 4.59E-17 M, produced two child groups fromthe parent group containing the total 67 samples. 47 samples with theTHSMB concentration<4.59E-17 M were assigned into “Normal-1”, while 20with THSMB concentration≥4.59E-17 M were assigned into “Cancer-1”. The“Normal-1” group was further partitioned by FTH1 with a threshold of8.44E-16 M. The resulting subgroups, “Normal-2” contained 28 sampleswith FTH1 concentration<8.44E-16 M, and “Cancer-2” contained 19 sampleswith FTH1 concentration≥8.44E-16 M. Consequently, the 67 serum samplesinvolved in our study were classified into the “Normal” group and the“Cancer” group by CART analysis.

The “Normal” group was composed of the samples from “Normal-2” whichincluded a total of 28 samples, 25 from normal subjects and 3 fromcancer patients. Thus, by using the combination of THSMB and FTH1 forOSCC prediction, the overall specificity is 78% (25/35). The “Cancer”group was composed of the samples from “Cancer-1” and “Cancer-2”. Thereare a total of 39 samples assigned in the final “Cancer” group, 29 fromcancer patients and 10 from normal subjects. Therefore, by using thecombination of these two serum mRNA for OSCC prediction, the overallsensitivity is 91% (29/32, in cancer group) and specificity is 78%(25/35, in normal group).

Example 13: RNA Isolation, Amplification and Gene Expression Profilingfrom Saliva of OSCC Patients Patient Selection.

OSCC patients were recruited from Medical Centers at University ofCalifornia, Los Angeles (UCLA) University of Southern California (USC),Los Angeles, Calif.; and University of California San Francisco, SanFrancisco, Calif.

Thirty-two patients with documented primary T1 or T2 OSCC were included.All of the patients had recently received diagnoses of primary diseaseand had not received any prior treatment in the form of chemotherapy,radiotherapy, surgery, or alternative remedies.

An equal number of age- and sex-matched subjects with comparable smokinghistories were selected as a control group. Among the two subjectgroups, there were no significant differences in terms of mean age: OSCCpatients, 49.8±7.6 years; normal subjects, 49.1±5.9 years (Student's ttest, P>0.80); gender (P>0.90); or smoking history (P>0.75). No subjectshad a history of prior malignancy, immunodeficiency, autoimmunedisorders, hepatitis, or HIV infection. All of the subjects signed theinstitutional review board-approved consent form agreeing to serve assaliva donors for the experiments.

Saliva Collection and RNA Isolation.

Unstimulated saliva samples were collected between 9 a.m. and 10 a.m.with previously established protocols [38]. Subjects were asked torefrain from eating, drinking, smoking, or oral hygiene procedures forat least 1 hour before the collection. Saliva samples were centrifugedat 2,600×g for 15 minutes at 4° C.

The supernatant was removed from the pellet and treated with RNaseinhibitor (Superase-In, Ambion Inc., Austin, Tex.). RNA was isolatedfrom 560 μl of saliva supernatant with QIAamp Viral RNA kit (Qiagen,Valencia, Calif.). Aliquots of isolated RNA were treated with RNase-freeDNase (DNasel-DNA-free, Ambion Inc.) according to the manufacturer'sinstructions. The quality of isolated RNA was examined by RT-PCR forthree cellular maintenance gene transcripts: glyceraldehyde-3-phosphatedehydrogenase (GAPDH), actin-β(ACTB), and ribosomal protein S9 (RPS9).Only those samples exhibiting PCR products for all three mRNAs were usedfor subsequent analysis.

On average, 54.2±20.1 ng (n=64) of total RNA was obtained from 560 μl ofsaliva supernatant. There was no significant difference in total RNAquantity between the OSCC and matched controls (t test, P=0.29, n=64).RT-PCR results demonstrated that all of the saliva samples (n=64)contained transcripts from three genes (GAPDH, ACTB, and RPS9), whichwere used as quality controls for human salivary RNAs [55]. A consistentamplifying magnitude (658±47.2, n=5) could be obtained after two roundsof RNA amplification. On average, the yield of biotinylated cRNA was39.3±6.0 pg (n=20). There were no significant differences or the cRNAquantity yielded between the OSCC and the controls (t test, P=0.31,n=20).

Example 14: Microarray Profiling of mRNA of Saliva from OSCC Patients

Saliva from 10 OSCC patients (7 male, 3 female; age, 52±9.0 years) andfrom 10 gender- and age-matched normal donors (age, 49±5.6 years) wasused for a microarray study. Isolated RNA from saliva was subjected tolinear amplification by RiboAmp RNA Amplification kit (Arcturus,Mountain View, Calif.). The RNA amplification efficiency was measured byusing control RNA of known quantity (0.1 pg) running in parallel withthe 20 samples in five independent runs.

Microarray Analysis.

Following previously reported protocols [55], the Human Genome U133AArray (HG U133A, Affymetrix, Santa Clara, Calif.) was applied for geneexpression analysis. The arrays were scanned and the fluorescenceintensity was measured by Microarray Suit 5.0 software (Affymetrix,Santa Clara, Calif.); the arrays were then imported into DNA-ChipAnalyzer software (http: www.dchp.org) for normalization and model-basedanalysis [60]. S-plus 6.0 (Insightful, Seattle, Wash.) was used to carryout all statistical tests.

Three criteria were used to determine differentially expressed genetranscripts. First, probe sets on the array that were assigned as“absent” call in all samples were excluded. Second, a two-tailedStudent's t test was used for comparison of average gene expressionsignal intensity between the OSCCs (n=10) and controls (n=10). Thecritical level of 0.05 was defined for statistical significance. Third,fold ratios were calculated for those gene transcripts that showedstatistically significant difference (P<0.05). Only those genetranscripts that exhibited at least 2-fold change were included forfurther analysis.

The HG U133A microarrays were used to identify the difference insalivary RNA profiles between cancer patients and matched normalsubjects. Among the 10,316 transcripts included by the previouslydescribed criteria, we identified 1,679 transcripts with P value lessthan 0.05. Among these transcripts, 836 were up-regulated and 843 weredown-regulated in the OSCC group. These transcripts observed wereunlikely to be attributable to chance alone (2 test, P<0.0001),considering the false positives with P<0.05. Using a predefined criteriaof a change in regulation >3-fold in all 10 OSCC saliva specimens and acutoff of P value<0.01, 17 mRNA, were identified showing significantup-regulation in OSCC saliva. 17 transcripts showed a change inregulation >3-fold in all 10 OSCC saliva specimens, and a more stringentcutoff of P value<0.01. It should be noted that these 17 salivary mRNAare all up-regulated in OSCC saliva, whereas there are no mRNAs founddown-regulated with the same filtering criteria. The biologicalfunctions of these genes and their products are presented in Table 7showing Salivary mRNA up-regulated (>3-fold, P<0.01) in OSCC identifiedby microarray

TABLE 7 Gene GenBank symbol Gene name accession No. Locus Gene FunctionsB2M _(—) β-2-microglobulin NM_04048 15q21- Antiapoptosis; antigen q22.2presentation DUSP1 Dual specificity NM_04417 5q34 Protein modification;signal phosphatase 1 transduction oxidative stress; FTH1 Ferritin, heavyNM_02032 11q13 Iron ion transport; cell polypeptide 1 proliferation GOS2Putative lymphocyte NM_015714 1q32.2- Cell growth and/or maintenance;GO-G1 switch gene q41 regulation of cell cycle GADD45B Growth arrest andNM_015675 19p13.3 Kinase cascade; apoptosis DNA-damage- inducible βH3F3A H3 histone, family BE869922 1q41 DNA binding activity 3A HSPC016Hypothetical BG167522 3p21.31 Unknown protein HSPC016 IER3 Immediateearly NM_003897 6p21.3 Embryogenesis; morphogenesis; response 3apoptosis; cell growth and maintenance IL1B Interleukin 1β M15330 2q14Signal transduction; proliferation; inflammation apoptosis IL8Interleukin 8 NM_000584 4q13-q21 Angiogenesis; replication;calcium-mediated signaling pathway; cell adhesion; chemotaxis cell cyclearrest; immune response MAP2K3 Mitogen-activated AA780381 17q11.2 Signaltransduction; protein protein kinase modification kinase 3 OAZ1Ornithine D87914 19p13.3 Polyamine biosynthesis decarboxylase antizyme 1PRG1 Proteoglycan 1, NM_002727 10q22.1 Proteoglycan secretory granuleRGS2 Regulator of G- NM_002923 1q31 Oncogenesis; G-protein signalprotein signaling transduction 2, 24 kda S100P S100 calcium NM_0059804p16 Protein binding; calcium ion binding protein P binding SATSpermidine/spermine NM_002970 Xp22.1 Enzyme, transferase activityN1-acetyltransferase EST highly similar BG537190 Iron ion homeostasis,ferritin ferritin light chain complex

The human Genome U133A microarrays were used to identify the differencein RNA expression patterns in saliva from 10 cancer patients and 10matched normal subjects. Using a criteria of a change inregulation >3-fold in all 10 OSCC saliva specimens and a cutoff of Pvalue<0.01, we identified 17 mRNA, showing significant up-regulation inOSCC saliva.

Example 15: Q-PCR Validation and Quantitation Analysis of MicroarrayProfiling from Cell-Free Saliva of OSCC Patients

Quantitative polymerase chain reaction (qPCR) was performed to validatea subset of differently expressed transcripts identified by themicroarray analysis of Example 14.

Quantitative Polymerase Chain Reaction Validation.

cDNA from the original and unamplified salivary RNA. was synthesizedUsing MuLV reverse transcriptase (Applied Biosystems, Foster City,Calif.) and random hexamers as primer (Applied Biosystems). The qPCRreactions were performed in an iCycler PCR system with IQ SYBR GreenSupermix (Bio-Rad, Hercules, Calif.). Primer sets were designed by usingPRIMER3 software (http://www.genome.wi.mit.edu).

All of the reactions were performed in triplicate with customizedconditions for specific products. The initial amount of cDNA/RNA of aparticular template was extrapolated from the standard curve asdescribed previously [32]. This validation completed by testing all ofthe samples (n=64) including those 20 previously used for microarraystudy. Wilcoxon Signed-Rank test was used for statistical analysis.

Quantitative PCR was performed to validate the microarray findings on anenlarged sample size including saliva from 32 OSCC patients and 32matched controls. Nine candidates of salivary mRNA biomarkers: DUSP1,GADD45B, H3F3A, IL1B, IL8, OAZ1, RGS2, S100P, and SAT were selectedbased on their reported cancer association reported in Table 7. Table 8presents the quantitative alterations of the above nine candidates insaliva from OSCC patients, determined by qPCR.

TABLE 8 Mean Gene P fold symbol Primer sequence (5′ to 3′) Validated *value increase DUSP1 F: SEQ ID NO: 35 Yes 0.039 2.60 R: SEQ ID NO: 36H3F3A F: SEQ ID NO: 37 Yes 0.011 5.61 R: SEQ ID NO: 38 IL1B F: SEQ IDNO: 39 Yes 0.005 5.48 R: SEQ ID NO: 40 IL8 F: SEQ ID NO: 41 Yes 0.00024.3 R: SEQ ID NO: 42 OAZ1 F: SEQ ID NO: 43 Yes 0.009 2.82 R: SEQ ID NO:44 S100P F: SEQ ID NO: 45 Yes 0.003 4.88 R: SEQ ID NO: 46 SAT F: SEQ IDNO: 47 Yes 0.005 2.98 R: SEQ ID NO: 48 GADD45B F: SEQ ID NO: 49 No 0.116R: SEQ ID NO: 50 RGS2 F: SEQ ID NO: 51 No 0.149 R: SEQ ID NO: 52 Sevenof the nine potential candidate were validated by qPCR (P < 0.05). *Wilcoxon's Signed Rank test: if P < 0.05, validated (Yes); if P .gtoreq.0.05, not validated (No)

The results confirmed that transcripts of 7 of the 9 candidate mRNA(78%), DUSP1, H3F3A, IL1B, IL8, OAZ1, S100P, and SAT, were significantlyelevated in the saliva of OSCC patient (Wilcoxon Signed-Rank test,P<0.05). We did not detect the statistically significant differences inthe amount of RGS2 (P=0.149) and GADD45B (P=0.116) by qPCR. Thevalidated seven genes could be classified in three ranks by themagnitude of increase: high up-regulated mRNA including IL8 (24.3-fold);moderate up-regulated mRNAs including H3F3A (5.61 fold), IL1B (5.48),and low up-regulated mRNAs including DUSP1 (2.60-fold), OAZ1(2.82-fold), and SAT (2.98-fold).

Example 16: ROC and Sensitivity/Specificity Analysis

Using the qPCR results, Receiver Operating Characteristic (ROC) curveanalyses was performed [82] by S-plus 6.0 to evaluate the predictivepower of each of the biomarkers identified in the Example 15.

Receiver Operating Characteristic Curve Analysis and Prediction Models.

The optimal cutpoint was determined for each biomarker by searching forthose that yielded the maximum corresponding sensitivity andspecificity. ROC curves were then plotted on the basis of the set ofoptimal sensitivity and specificity values. Area under the curve wascomputed via numerical integration of the ROC curves. The biomarker thathas the largest area under the ROC curve was identified as having thestrongest predictive power for detecting OSCC.

Next, multivariate classification models were constructed to determinethe best combination of salivary markers for cancer prediction. Firstly,using the binary outcome of the disease (OSCC) and nondisease (normal)as dependent variables, we constructed a logistic regression modelcontrolling for patient age, gender, and smoking history. The backwardstepwise regression [61] was used to find the best final model.

Leave-one-out cross-validation was used to validate the logisticregression model. The cross-validation strategy first removes oneobservation and then fits a logistic regression model from the remainingcases with all of the markers. Stepwise model selection is used for eachof these models to remove variables that do not improve the model.Subsequently, the marker values were used for the case that was left outto compute a predicted class for that observation. The cross-validationerror rate is then the number of samples predicted incorrectly dividedby the number of samples.

The ROC curve, illustrated in FIG. 11, was then computed for thelogistic model by a similar procedure, with the fitted probabilitiesfrom the model as possible cutpoints for computation of sensitivity andspecificity.

The detailed statistics of the area under the receiver operatorcharacteristics (ROC) curves, the threshold values, and thecorresponding sensitivities and specificities for each of the sevenpotential salivary mRNA biomarkers for OSCC are listed in Table 9showing the ROC curve analysis of OSCC-associated salivary mRNAbiomarkers

TABLE 9 Area under Threshold/cutoff Sensitivity Specificity BiomarkerROC curve (M) (%) (%) DUSP1 0.65 8.35E−17 59 75 H3F3A 0.68 1.58E−15 5381 IL1B 0.70 4.34E−16 63 72 IL8 0.85 3.19E−18 88 81 OAZ1 0.69 7.42E−17100 38 S100P 0.71 2.11E−15 72 63 SAT 0.70 1.56E−15 81 56

Utilizing the qPCR results, we conducted ROC curve analyses to evaluatethe predictive power of each of the biomarkers. The optimal cutpoint wasdetermined yielding the maximum corresponding sensitivity andspecificity. The biomarker that has the largest area under the ROC curvewas identified as having the strongest predictive power for detectingOSCC.

The data showed IL8 mRNA performed the best among the seven potentialbiomarkers for predicting the presence of OSCC. The calculated areaunder the ROC curve for IL8 was 0.85. With a threshold value of 3.19E-18mol/L, IL8 mRNA in saliva yields a sensitivity of 88% and a specificityof 81% to distinguish OSCC from the normal.

To demonstrate the utility of salivary mRNAs for disease discrimination,two classification/prediction models were examined. A logisticregression model was built based on the four of the seven validatedbiomarkers, IL1B, OAZ1, SAT, and IL8, which in combination provided thebest prediction (Table 10). Table 10 shows salivary for OSCC selected bylogistic regression model

TABLE 10 Biomarker Coefficient value SE P value Intercept −4.79 1.510.001 IL1B 5.10E+19 2.68E+19 0.062 OAZ1 2.18E+20 1.08E+20 0.048 SAT2.63E+19 1.10E+19 0.020 IL8 1.36E+17 4.75E+16 0.006

The logistic regression model was built based on the four of sevenvalidated biomarkers (IL1B, OAZ1, SAT, and IL8) that, in combination,provided the best prediction. The coefficient values are positive forthese four markers, indicating that the synchronized increase in theirconcentrations in saliva increased the probability that the sample wasobtained from an OSCC subject.

The coefficient values are positive for these four markers, indicatingthat the synchronized rise in their concentrations in saliva increasedthe probability that the sample was obtained from an OSCC subject. Theleave-one-out cross-validation error rate based on logistic regressionmodels was 19% (12 of 64). All but one (of the 64) of the modelsgenerated in the leave-one-out analysis used the same set of fourmarkers found to be significant in the full data model specified inTable 10.

The ROC curve was computed for the logistic regression model. Using acutoff probability of 50%, we obtained a sensitivity of 91% and aspecificity of 91%. The calculated area under the ROC curve was 0.95 forthe logistic regression model (FIG. 11).

Tree-Based Classification Model, Classification and Regression Tree(CART),

A second model, a tree-based classification model, classification andregression tree (CART) model,” was generated. The CART model wasconstructed by S-plus 6.0 with the validated mRNA biomarkers aspredictors. CART fits the classification model by binary recursivepartitioning, in which each step involves searching for the predictorvariable that results in the best split of the cancer versus the normalgroups [62]. CART used the entropy function with splitting criteriadetermined by default settings for S-plus. By this approach, the parentgroup containing the entire samples (n=64) was subsequently divided intocancer groups and normal groups. Our initial tree was pruned to removeall splits that did not result in sub-branches with differentclassifications.

Results are shown in the diagram of FIG. 12. Our fitted CART model usedthe salivary mRNA concentrations of IL8, H3F3A, and SAT as predictorvariables for OSCC. IL8, chosen as the initial split, with a thresholdof 3.14E_18 mol/L, produced two child groups from the parent groupcontaining the total 64 samples. 30 samples with the IL8concentration<3.14E-18 mol/L were assigned into “Normal-1,” whereas 34with IL8 concentration≥3.14E-18 were assigned into “Cancer-1”. The“Normal-1” group was further partitioned by SAT with a threshold of1.13E-14 mol/L.

The resulting subgroups, “Normal-2” contained 25 samples with SATconcentration<1.13E-14 mol/L, and “Cancer-2” contained 5 samples withSAT concentration≥0.1.13E-14 mol/L. Similarly, the “Cancer-1” group wasfurther partitioned by H3F3A with a threshold of 2.07E-16 mol/L. Theresulting subgroups, “Cancer-3” contained 27 samples with H3F3Aconcentration≥2.07E-16 mol/L, and “Normal-3” group contained 7 sampleswith H3F3A concentration<2.07E-16 mol/L.

Consequently, the 64 saliva samples involved in our study wereclassified into the “Cancer” group and the “Normal” group by CARTanalysis. The “Normal” group was composed of the samples from “Normal-2”and those from “Normal-3”. There are a total of 32 samples assigned inthe “Normal” group, 29 from normal subjects and 3 from cancer patients.

Thus, by using the combination of IL8, SAT, and H3F3A for OSCCprediction, the overall sensitivity is 90.6% (29 of 32). The “Cancer”group was composed of the samples from “Cancer-2 and Cancer-3”. Thereare a total of 32 samples assigned in the final “Cancer” group, 29 fromcancer patients and 3 from normal subjects. Therefore, by using thecombination of these three salivary mRNA biomarkers for OSCC prediction,the overall specificity is 90.6% (29 of 32).

In summary the present disclosure refers to a method to detect abiomarker in saliva wherein the biomarker is an extracellular mRNA,comprises detecting the extracellular mRNA in the cell-free saliva;transcriptome analysis of saliva comprises detecting a transcriptomepattern in the cell-free saliva; a method to detect genetic alterationsin an organ or in a gene in the organ by analyzing saliva, comprisesdetecting a transcriptome pattern and/or the mRNA profiling of the genein cell-free saliva; a method to diagnose an oral or systemic pathologydisease or disorder in a subject, comprises: detecting profile of abiomarker associated with the pathology disease or disorder, inparticular mRNA and/or protein, in cell-free saliva and/or serum; kitscomprising identifier for at least one biomarker for performing at leastone of the methods; and use of salivary biomarker salivary and/or serummRNAs as biomarkers for oral and/or systemic pathology, disease ordisorder.

The disclosures of each and every publication and reference cited hereinare hereby incorporated herein by reference in their entirety.

The present disclosure has been explained with reference to specificembodiments. Other embodiments will be apparent to those of ordinaryskill in the art in view of the foregoing description. The scope ofprotection of the present disclosure is defined by the appended claims.

REFERENCES

-   [1] Parkin M, Pisani P, Ferlay J. Estimates of the worldwide    incidence of 25 major cancers in 1990. Int J. Cancer. 1999;    80:827-841.-   [2] Goeptert H. squamous cell carcinoma or the head and neck, past    progress and future promise. CA Cancer J Clin. 1998; 48:195-198.-   [3] Sidransky D. Emerging molecular markers of cancer. Nat Reviews.    2002; 3:210-219.-   [4] Alevizos I, Mahadevappa M, Zhang X, et al. Oral cancer in vivo    gene expression profiling assisted by laser-capture microdissection    and microarray analysis. Oncogene. 2001; 20:6196-6204.-   [5] Chen Z, Malhotra P S, Thomas G R, et al. Expression of    proinflammatory and proangiogenic cytokines in patients with head    and neck cancer. Clin Cancer Res. 1999; 5:1369-1379.-   [6] Sidransky D. Nucleic acid-based methods for the detection of    cancer. Science. 1997; 278: 1054-1058.-   [7] Navazesh M, Christensen C A. A comparison of whole mouth resting    and stimulated salivary measurements. J. Dent. Res. 1982;    61:1158-1162.-   [8] Hanley J A, McNeil B J. The meaning and use of the area under a    receiver operating characteristic (ROC) curve. Radiology. 1982;    143:29-36.-   [9] Hanley J A, McNeil B J. A method of comparing the areas under    receiver operating characteristic curves derived from the same    cases. Radiology. 1983; 148:839-843.-   [10] Liu H H, Wu T T. Estimating the area under a receiver operating    characteristic (ROC) curve for repeated measures design. Journal of    Statistical Software. 2003; 8:1-18.-   [11] Norton J A, Peacock J L, Morrison S D. Cancer cachexia. Crit    Rev Oncol Hematol. 1987; 7:289-327.-   [12] Smith D R, Polverini P J, Kunkel S L, et al. Inhibition or    interleukin 8 attenuates angiogenesis in bronchogenic carcinoma. J    Exp Med. 1994; 179:1409-1415.-   [13] Dong G, Loukinova E, Smith C W, Chen Z, and Van Waes C. Genes    differentially expressed with malignant transformation and    metastatic tumor progression of murine squamous cell carcinoma. J    Cell Biochem Suppl. 1997; 28/29:90-100.-   [14] Pak A S, Wright M A, Matthews J P, et al. Mechanisms of immune    suppression in patients with head and neck cancer: presence of CD34+    cells which suppress immune functions within cancers that secrete    granulocyte-macrophage colony-stimulating factor. Clin Cancer Res.    1995; 1:95-103.-   [15] Ueda T, Shimada E, Urakawa T. Serum levels of cytokines in    patients with colorectal cancer: possible involvement of    interleukin-6 and interleukin-8 in hematogenous metastasis. J.    Gastroenterol. 1994; 29:423-429.-   [16] Oka M, Yamamoto K, Takahashi M, et al. Relationship between    serum levels of interleukin 6, various disease parameters, and    malnutrition in patients with esophageal squamous cell. carcinoma.    Cancer Res. 1996; 56:2776-2780.-   [17] Wang P L, Ohura K, Fujii T, Oido-Mori M, Kowashi Y, Kikuchi M,    Suetsugu Y, Tanaka J. DNA microarray analysis of human gingival    fibroblasts from healthy and inflammatory gingival tissues. Biochem    Biophys Res Commun. 2003: 305:970-973.-   [18] Giannopoulou C, Kamma J J, Mombelli A. Effect of inflammation,    smoking and stress on gingival crevicular fluid cytokine level. J    Clin Periodontol. 2003; 30:145-153.-   [19] Sullivan Pepe M, Etzioni R, Feng Z, et al. Phases of biomarker    development for early detection of cancer. J National Can Inst.    2001; 93:1054-1061.-   [20] van Houten V M, Tabor M P, van den Berker M W, et al. Molecular    assays for the diagnosis of minimal residual head-and-neck cancer:    methods, reliability, pitfalls, and solutions. Clin Cancer Res.    2000; 6:3803-3816.-   [21] Thompson M L, Zucchini W. On the statistical analysis of ROC    curves. Stat Med. 1989; 8:1277-1290.-   [22] Hoffman H T, Karnell L H, Funk G F, Robinson R A, Menck H R.    The national cancer data base report on cancer of the head and neck.    Arch Otolaryngol Head Neck Surg. 1998; 124:951-962.-   [23] Khuri F R, Shin D M, Glisson B S, Lippman S M, Hong W K.    Treatment of patients with recurrent or metastatic squamous cell    carcinoma of the head and neck: current status and future    directions. Semin Oncol. 2000; 27:25-33.-   [24] Spafford M F, Koch W M, Reed A L, et al. Detection of head and    neck squamous cell carcinoma among exfoliated oral cells by    microsatellite analysis. Clin Cancer Res. 2001; 7:607-612.-   [25] Bradford C R. Genetic markers of head and neck cancer:    identifying new molecular targets. Arch Otolaryngol Head Neck Surg.    2003; 129:366-367.-   [26] Koch W M. Genetic markers in the clinical care of head and neck    cancer: slow in coming. Arch Otolaryngol Head Neck Surg. 2003;    129:367-368.-   [27] Affymetrix, (2001). Affymetrix Technical Note: New Statistical    Algorithms for Monitoring Gene Expression on GeneChip® Probe Arrays.    Santa Clara, Calif.: Affymetrix.-   [28] Anker P, Mulcahy H, Chen X Q, Stroun M (1999). Detection of    circulating tumor DNA in the blood (plasma/serum) of cancer    patients. Cancer Metastasis Rev 18(1):65-73.-   [29] Anker P, Mulcahy H, Stroun M (2003). Circulating nucleic acids    in plasma and serum as a noninvasive investigation for cancer: time    for large-scale clinical studies? Int J Cancer 103(2): 149-152.-   [30] Bonassi 5, Neri M, Puntoni R (2001). Validation of biomarkers    as early predictors of disease. Mutat Res 480-481: 349-358.-   [30] El-Naggar A K, Mao L, Staerkel G, Coombes M M, Tucker S L, Luna    M A, et al (2001). Genetic heterogeneity in saliva from patients    with oral squamous carcinomas: implications in molecular diagnosis    and screening. J Mol Diagn 3(4): 164-170.-   [31] Fleischhacker M. Beinert T, Ermitsch M. Seferi D, Possinger K,    Engelmann C, et al (2001). Detection of amplifiable messenger RNA in    the serum of patients with lung cancer. Ann NY Acad Sci 945:    i79-188.-   [32] Ginzinger D (2002). Gene quantification using real-time    quantitative PCR: an emerging technology hits the mainstream. Exp    Hemato 30: 503-512.-   [33] Kelly J J, Chernov B K, Tovstanovsky I, Mirzabekov A D, Bavykin    S G (2002). Radical-generating coordination complexes as tools for    rapid and effective fragmentation and fluorescent labeling of    nucleic acids for microchip hybridization. Anal Biochem 311(2):    103-118.-   [34] Kopreski M S, Benko F A, Kwak L W, Gocke C D (1999). Detection    of tumor messenger RNA in the serum of patients with malignant    melanoma. Clin Cancer Res 5:1961-1965.-   [35] Lawrence H P (2002). Salivary markers of systemic disease:    noninvasive diagnosis of disease and monitoring of general health. J    Can Dent Assoc 68(3):170-174.-   [36] Liao P H, Chang Y C, Huang M F, Tai K W, Chou M Y (2000).    Mutation of p53 gene codon 63 in saliva as a molecular marker for    oral squamous cell carcinomas. Oral Oncol 36(3):272-276.-   [37] Mercer D K, Scott K P, Melville C M, Glover L A, Flint H J    (2001). Transformation of an oral bacterium via chromosomal    integration of free DNA in the presence of human saliva. FEMS    Microbiol Lett 200(2): 163-167.-   [38] Navazesn M (1993). Methods for collecting saliva. Ann N Y Acad    Sci 694:72-77.-   [39] Ohyama H, Zhang X, Kohno Y, Alevizos I, Posner M, Wong D T, et    al (2000). Laser capture microdissection-generated target sample for    high-density oligonucleotide array hybridization. Biotechniques    29(3): 530-536.-   [40] Pusch W, Flocco M T. Leung S M, Thiele H, Kostrzewa M (2003).    Mass spectrometry-based clinical proteomics. Pharmacogenomics    4(4):463-476.-   [41] Rehak N N, Cccco S A, Csako G (2000). Biochemical composition    and electrolyte balance of “unstimulated” whole human saliva. Clin    Chem Lab Med 38(4):335-343.-   [42] Rieger-Christ K M, Mourtzinos A, Lee P J, Zagha R M, Cain J,    Silverman M, et al (2003). Identification of fibroblast growth    factor receptor 3 mutations in urine sediment DNA samples    complements cytology in bladder tumor detection. Cancer    98(4):737-744.-   [43] Sakki T, Knuuttila M (1996). Controlled study of the    association of smoking with lactobacilli, mutans streptococci and    yeasts in saliva. Eur J Oral Sci 104(5-6):619-622.-   [44] Horer, O. L. and G. Palicari, RNase activity in the capillary    blood of children. Virologie, 1986. 37(2): p. 111-114-   [45] Sidransky D (2002). Emerging molecular markers of cancer. Nat    Reviews 3:210-219.-   [46] Stamey F R, DeLeon-Carnes M, Patel M M, Pellett P E, Dollard S    C (2003). Comparison of a microtiter plate system to Southern blot    for detection of human herpesvirus 8 DNA amplified from blood and    saliva. J Virol Methods 108(2):189-193.-   [47] Streckfus C F, Bigler L R (2002). Saliva as a diagnostic fluid.    Oral Dis 8(2):69-76.-   [48] Venter J C, Adams M D, Myers E W, Li P W, Mural R J, Sutton G G    et al (2001). The sequence of the human genome (published erratum    appears in Science 292(5523): 1838). Science 291(5507): 1304-1351.-   [49] Wong U, Lueth M, Li X N, Lau C C, Vogel H (2003). Detection of    mitochondrial DNA mutations in the tumor and cerebrospinal fluid of    medulloblastoma patients. Cancer Res 63(14):3866-3871.-   [50] Xiang C C, Chen M, Ma L, Phan Q N, Inman J M, Kozhich O A, et    al (2003). A new strategy to amplify degraded RNA from small tissue    samples for microarray studies. Nucleic Acids Res 31(9):e53-   [51] Lipshutz, R. J., et al., High density synthetic oligonucleotide    arrays. Nat Genet, 1999. 21(1 Suppl): p. 20-4.-   [52] Lipshutz, R. J., Applications of high-density oligonucleotide    arrays. Novartis Found Symp, 2000. 229: p. 84-90; discussion 90-3.-   [53] Barker, P. E., Cancer biomarker validation: standards and    process: roles for the National Institute of Standards and    Technology (NIST). Ann N Y Acad Sci, 2003. 983: p. 142-50.-   [54] Juusola, J. and J. Ballantyne, Messenger RNA profiling: a    prototype method to supplant conventional methods for body fluid    identification. Forensic Sci Int, 2003. 135(2): p. 85-96.-   [55] Li, Y., et al., RNA profiling of cell-free saliva using    microarray technology. J Dent Res, 2004. 83(3): p. 199-203.-   [56] Kopreski, M. S., F. A. Benko, and C. D. Gocke, Circulating RNA    as a tumor marker: detection of 5T4 mRNA in breast and lung cancer    patient serum. Ann N Y Acad Sci, 2001. 945: p. 172-178.-   [57] Bunn, P. J., Jr., Early detection of lung cancer using serum    RNA or DNA markers: ready for “prime time” or for validation? J Clin    Oncol, 2003. 21(21): p. 3891-3893.-   [58] Wong, S. C., et al., Quantification or plasma beta-carenin mRNA    in colorectal cancer and adenoma patients. Clin Cancer Res, 2004.    10(5): p. 1613-1617.-   [59] Fugazzola, L., et al., Highly sensitive serum thyroglobulin and    circulating thyroglobulin mRNA evaluations in the management of    patients with differentiated thyroid cancer in apparent remission. J    Clin Endocrinol Metab, 2002. 87(7): p. 3201-8.-   [60] Li, C. and W. H. Wong, Model-based analysis of oligonucleotide    arrays: expression index computation and outlier detection. Proc    Natl Acad Sci USA, 2001.98(1): p. 31-36.-   [61] Renger, R. and L. M. Meadows, Use of stepwise regression in    medical education research. Acad Med, 1994. 69(9): p. 738.-   [62] Lemon, S. C., et al., Classification and regression tree    analysis in public health: methodological review and comparison with    logistic regression. Ann Behav Med, 2003. 26(3): p. 172-181.-   [63] Cancer facts and figures 2004. Atlanta: American Cancer    Society, 2004.-   [64] Wildt, J., T. Bundgaard, and S. M. Bentzen, Delay in the    diagnosis of oral squamous cell carcinoma. Clin Otolaryngol, 1995.    20(1): p. 21-25-   [65] Fong, K. M., et al., Molecular genetic basis for early cancer    detection and cancer susceptibility, in Molecular Pathology of Early    Cancer, S. S. HD and G. AF, Editors. 1999, IOS Press. p. 13-26.-   [66] Epstein, J. B., L. Zhang, and M. Rosin, Advances in the    diagnosis of oral premalignant and malignant lesions. J Can Dent    Assoc, 2002. 68(10): p. 617-621.-   [67] Mao, L., W. K. Hong, and V. A. Papadimitrakopoulou, Focus on    head and neck cancer. Cancer Cell, 2004. 5(4): p. 311-316-   [68] Li, Y., et al., Salivary transcriptome diagnostics for oral    cancer detection. Clin Cancer Res, 2004. 10(24): p. 8442-8450.-   [69] Ng, E. K., et al., Presence of filterable and nonfilterable    mRNA in the plasma of cancer patients and healthy individuals. Clin    Chem, 2002. 48(8): p. 1212-1217-   [70] Jung, K., et al., Increased cell-free DNA in plasma of patients    with metastatic spread in prostate cancer. Cancer Lett, 2004.    205(2): p. 173-180-   [71] Wang, B. G., et al., Increased plasma DNA integrity in cancer    patients. Cancer Res, 2003. 63(14): p. 3966-3968.-   [72] Lo, Y. M., et al., Quantitative analysis of cell-free    Epstein-Barr virus DNA in plasma of patients with nasopharyngeal    carcinoma. Cancer Res, 1999. 59(6): p. 1188-1191-   [73] Chen, X. Q., et al., Telomerase RNA as a detection marker in    the serum of breast cancer patients. Clin Cancer Res, 2000.    6(10): p. 3823-3826.-   [74] Silva, J. M., et al., Detection of epithelial messenger RNA in    the plasma of breast cancer patients is associated with poor    prognosis tumor characteristics. Clin Cancer Res, 2001. 7(9): p.    2821-2825.-   [75] Dasi, F., et al., Real-time quantification in plasma of human    telomerase reverse transcriptase (hTERT) mRNA: a simple blood test    to monitor disease in cancer patients. Lab Invest, 2001. 81(5): p.    767-769-   [76] Bernard, P. S. and C. T. Wittwer, Real-time PCR technology for    cancer diagnostics. Clin Chem, 2002. 48(8): p. 1178-1185.-   [77] Jahr, S., et al., DNA fragments in the blood plasma of cancer    patients: quantitations and evidence for their origin from apoptotic    and necrotic cells. Cancer Res, 2001. 61(4): p. 1659-1665-   [78] Stroun, M., et al., Neoplastic characteristics of the DNA found    in the plasma of cancer patients. Oncology, 1989. 46(5): p. 318-322-   [79] Hollstein M, Sidransky D, Vogelstein B, Harris C C. p53    mutations in human cancers. Science (Wash D.C.) 1991; 253:49-53.-   [80] Liu T, Wahlbcrg S, Burek E, Lindblom P, Rubio C, Lindblom A.    Microsatellite instability as a predictor of a mutation in a DNA    mismatch repair gene in familial colorectal cancer. Genes    Chromosomes Cancer 2000; 27: 17-25.-   [81] Groden J, Thliveris A, Samowitz W, et al. Identification and    characterization of the familial adenomatous polyposis coli gene.    Cell 1991; 66:589-600.-   [82] Grunkerneler G L, Jin R. Receiver operating characteristic    curve analysis of clinical risk models. Ann Thorac Surg 2001;    72:323-326.-   [83]. Kharchenko S V, Shpakov A A. [Regulation of the RNase activity    of the saliva in healthy subjects and in stomach cancer]. Izv Akad    Nauk SSSR Biol 1989:58-63.-   [84]. Myers L L, Wax M K. Positron emission tomography in the    evaluation of the negative neck in patients with oral cavity cancer.    J Otolaryngol 1998; 27:342-347.-   [85] Mashberg A, Samit A. Early diagnosis of asymptomatic oral and    oropharyngeal squamous cancers. CA Cancer J Clin 1995; 45:328-51.-   [86] Rosin M P, Epstein J B, Berean K, et al. The use of exfoliative    cell samples to map clonal genetic alterations in the oral    epithelium of high-risk patients. Cancer Res 1997; 57:5258-5260.-   [87] Streckfus C, Bigler L, Dellinger T, Dai X, Kingman A, Thigpen    J T. The presence of soluble c-erbB-2 in saliva and serum among    women with breast carcinoma: a preliminary study. Clin Cancer Res    2000; 6: 2363-2370.-   [88] Unoki M, Nakamura Y. Growth-suppressive effects of BPO2 and    EGR2, two genes involved in the PTEN signaling pathway. Oncogene    2001; 20:4457-4465.-   [89] Suzuki C, Unoki M, Nakamura Y. Identification and allelic    frequencies of novel single-nucleotide polymorphisms in the DUSP1    and BTG 1 genes. J Hum Genet 2001; 46:155-157.-   [90] Bettuzzi S, Davalli P, Astancolle S, et al. Tumor progression    is accompanied by significant changes in the levels of expression of    polyamine metabolism regulatory genes and clusterin (sulfated    glycoprotein 2) in human prostate cancer specimens. Cancer Res 2000;    60: 28-34.-   [91] Torelli G, Venturelli D, Colo A, et al. Expression of c-myb    protooncogene and other cell cycle-related genes in normal and    neoplastic human colonic mucosa. Cancer Res 1987; 47:5266-5269.-   [92] Tsuji T, Usui S, Aida T, et al. Induction of epithelial    differentiation and DNA demethylation in hamster malignant oral    keratinocyte by ornithine decarboxylase antizyrne. Oncogene 2001;    20:24-33.-   [93] Gribenko A, Lopez M M, Richardson J M III, Makhatadze G I.    Cloning, overexpression, purification, and spectroscopic    characterization of human S100P. Protein Sci 1998; 7:211-215.-   [94] Guerreiro Da Silva I D, Hu Y F, Russo I H, et al. S100P    calciumbinding protein overexpression is associated with    immortalization of human breast epithelial cells in vitro and early    stages of breast cancer development in vivo. Int J Oncol 2000;    16:231-240.-   [95] Mousses S, Bubendorf L, Wagner U, et al. Clinical validation of    candidate genes associated with prostate cancer progression in the    CWR22 model system using tissue microarrays. Cancer Res 2002; 62:    1256-1260.-   [96] Mackay A, Jones C. Dexter I, et al. cDNA microarray analysis or    genes associated with ERBB2 (HER2/neu) overexpression in human    mammary luminal epithelial cells. Oncogene 2003; 22:2680-2688.-   [97] Logsdon C D, Simeone D M, Binkley C, et al. Molecular profiling    of pancreatic adenocarcinoma and chronic pancreatitis identifies    multiple genes differentially regulated in pancreatic cancer. Cancer    Res 2003; 63: 2649-2657.-   [98] Crnogorac-Jurcevic T, Missiaglia E, Blayeri E, at al. Molecular    alterations in pancreatic carcinoma: expression profiling shows that    dysregulated expression of S100 genes is highly prevalent. J Pathol    2003; 201:63-74.-   [99] Jablonska E, Piotrowski L, Grabowska Z. Serum Levels of IL-lb,    IL6, TNF-a, sTNF-RI and CRP in patients with oral cavity cancer.    Pathol Oncol Res 1997; 3:126-129.-   [100] Chen C K, Wu M Y, Chao K H, Ho H N, Sheu B C, Huang S C. T    lymphocytes and cytokine production in ascitic fluid of ovarian    malignancies. J Formos Med Assoc 1999; 98:24-30.-   [101] Hamajima N, Yuasa H. [Genetic polymorphisms related to    interleukin-1 beta production and disease risk]. Nippon Koshu Eisei    Zasshi 2003; 50:194-207.-   [102] El-Omar E M, Rabkin C S, Gammon M D, et al. Increased risk of    noncardia gastric cancer associated with proinflammatory cytokine    gene polymorphisms. Gastroenterology 2003; 124:1193-1201.-   [103] Malamud D. Oral diagnostic testing for detecting human    immunodeficiency virus-1 antibodies: a technology whose time has    come. Am J Med 1997; 102:9-14.-   [104] Guven Y, Satman I, Dinccag N, Alptekin S. Salivary peroxidase    activity in whole saliva of patients with insulin-dependent (type-1)    diabetes mellitus. J Clin Periodontol 1996; 23:879-881.-   [105] Lee J J, Hong W K. Hillerman W N, et al. Predicting cancer    development in oral leukoplakia: ten years of translational    research. Clin Cancer Res 2000; 6:1702-1710.-   [106] Silverman S Jr, Gorsky M. Proliferative verrucous leukoplakia:    a follow-up study of 54 cases. Oral Surg Oral Med Oral Pathol Oral    Radiol Endod 1997; 84:154-157.-   [107] Sudbo J, Kildal W, Risberg B, Koppang H S, Danielsen H E,    Reith A. DNA content as a prognostic marker in patients with oral    leukoplakia. N Engl J Med 2001; 344:1270-1278.-   [108] Berta G N, Ghezzo F, D'Avolio A, et al. Enhancement of    calcyclin gene RNA expression in squamous cell carcinoma of the oral    mucosa, but not in benign lesions. J Oral Pathol Med 1997;    26:206-210.-   [109] Watanabe H, Iwase M, Ohashi M, Nagumo M. Role of interleukin-8    secreted from human oral squamous cell carcinoma cell lines. Oral    Oncol 2002; 38:670-679.

1. A method to detect an extracellular mRNA in a bodily fluid, thebodily fluid including a cell phase and a fluid phase, the methodcomprising: providing a cell-free fluid phase portion of the bodilyfluid; and detecting the extracellular mRNA in the cell-free fluid phaseportion of the bodily fluid, wherein the bodily fluid is saliva.
 2. Themethod of claim 1, wherein the bodily fluid is unstimulated saliva. 3.The method of claim 1 wherein detecting the extracellular mRNA comprisesisolating the extracellular mRNA from the cell-free fluid phase portionof the bodily fluid and amplifying the extracellular mRNA. 4-9.(canceled)
 10. A method to diagnose an oral or systemic pathology,disease or disorder in a subject, the method comprising: providing acell-free fluid phase portion of the saliva of the subject; detecting inthe provided cell-free saliva fluid phase portion an mRNA profile of agene associated with the pathology, disease or disorder; and comparingthe RNA profile of the gene with a predetermined mRNA profile of thegene, the predetermined mRNA profile of the gene being indicative of thepresence of the pathology, disease or disorder in the subject.
 11. Themethod of claim 10, wherein the disease is a cancer of the oral cavityand/or of oropharynx and the gene is selected from the group consistingof the gene coding for IL8, DUSP1, H3F3A, OAZ1, S100P and SAT.
 12. Themethod of claim 10, wherein the disease is a cancer of the oral cavityand/or oropharynx and the gene is the gene coding for
 18. 13. The methodof claim 12, wherein the disease is oropharyngeal squamous cellcarcinoma or head and neck squamous cell carcinoma. 14-16. (canceled)17. A method for diagnosing a cancer in a subject, the methodcomprising: providing a bodily fluid of the subject; detecting in thebodily fluid a profile or a biomarker, the biomarker selected from thegroup consisting of IL6, H3F3A, TPT1, FTH1, NCOA4 and ARCR, comparingthe profile of the biomarker with a predetermined profile of thebiomarker, recognition in the profile of the biomarker ofcharacteristics of the predetermined profile of the biomarker beingdiagnostic for the cancer. 18-19. (canceled)
 20. The method of claim 17,wherein the cancer is the oropharyngeal squamous cell carcinoma or headand neck squamous cell carcinoma, the biomarker is IL6, the bodily fluidis blood serum and detecting a profile of a biomarker is performed bydetecting the protein profile of the biomarker. 21-27. (canceled)